Responsible research and innovation imposes normative requirements on research and innovation processes resembling three successive steps, each more ambitious than its predecessor, with distinct features. For the research dimension the distinct features reflect the normative requirements of, first, credible research ; second, responsive research ; and third, responsible research. Equally distinct features reflect the requirements of credible innovation, responsive innovation, and responsible innovation.

In this article, I will explore how the underlying research values of ‘openness’ and ‘mutual responsiveness’, which are central to open science practices, can be integrated into a new ethos of science. Firstly, I will revisit Robert Merton's early contribution to this issue, examining whether the ethos of science should be understood as a set of norms for scientists to practice ‘good’ science or as a set of research values as a functional requirement of the scientific system to produce (...) knowledge, irrespective of individual adherence to these norms. Secondly, I will analyse the recent codification of scientific practice in terms of ‘scientific integrity’, a framework that Merton did not pursue. Based on this analysis, and illustrated on the case of COVID-19 as a case in which the institution of science was challenged to deliver urgently on societal desirable outcomes, I will argue that promoting open science and its core norms of collaboration and openness requires broader governance of the institution of science in its relationship with society at large, rather than relying solely on self-governance within the scientific community through a new ethos of science. This conclusion has implications for re-evaluating research assessments, suggesting that the evaluation of the scientific system should take precedence over evaluating individual researchers, and that incentives should be provided to encourage specific research behaviour rather than solely focusing on individual research outputs. This article is followed up by Commentaries of Carl Mitcham, Alfred Nordmann, Sabina Leonelli, Lukas Fuchs, Monica Edwards-Schachter. (shrink)

The fields of Responsible Research and Innovation (RRI) and participatory foresight seek to establish, and to include publics within, anticipatory governance mechanisms. While scenario-based methods can bring to the publics’ attention the ethical challenges associated to existing technologies, there has been little empirical research examining how, in practice, prospective public deliberative processes should be organized to inform anticipatory governance. The goal of this article is to generate methodological insights into the way such methods can stimulate (...) the public's moral imagination regarding what may (or may not) happen in the future and what should (or should not) happen in the future. Our qualitative analyses draw on a public deliberation study that included videos and online scenarios to support participants’ (n = 57) deliberations about fictional interventions for genetically at-risk individuals. Our findings clarify how participants: (1) challenged key elements of our scenarios; (2) extended several of their technical and moral prospects; (3) engaged personally with others, including our scenarios’ characters; and (4) mobilized the past creatively to reason about the future. Our methodology enabled participants to creatively and empathetically envision complex sociotechnical futures. Yet, important methodological limits should be acknowledged by those who design, implement and use public engagement methods to inform anticipatory governance. (shrink)

Historically, scientific and engineering expertise has been key in shaping research and innovation policies, with benefits presumed to accrue to society more broadly over time. But there is persistent and growing concern about whether and how ethical and societal values are integrated into R&I policies and governance, as we confront public disbelief in science and political suspicion toward evidence-based policy-making. Erosion of such a social contract with science limits the ability of democratic societies to deal with challenges presented by (...) new, disruptive technologies, such as synthetic biology, nanotechnology, genetic engineering, automation and robotics, and artificial intelligence. Many policy efforts have emerged in response to such concerns, one prominent example being Europe's Eighth Framework Programme, Horizon 2020, whose focus on “Responsible Research and Innovation” provides a case study for the translation of such normative perspectives into concrete policy action and implementation. Our analysis of this H2020 RRI approach suggests a lack of consistent integration of elements such as ethics, open access, open innovation, and public engagement. On the basis of our evaluation, we suggest possible pathways for strengthening efforts to deliver R&I policies that deepen mutually beneficial science and society relationships. (shrink)

Open science will make science more efficient, reliable, and responsive to societal challenges. The European Commission has sought to advance open science policy from its inception in a holistic and integrated way, covering all aspects of the research cycle from scientific discovery and review to sharing knowledge, publishing, and outreach. We present the steps taken with a forward-looking perspective on the challenges laying ahead, in particular the necessary change of the rewards and incentives system for researchers (for which (...) various actors are co-responsible and which goes beyond the mandate of the European Commission). Finally, we discuss the role of artificial intelligence (AI) within an open science perspective. (shrink)

In the last decade Open Science principles, such as Open Access, study preregistration, use of preprints, making available data and code, and open peer review, have been successfully advocated for and are being slowly adopted in many different research communities. In response to the COVID-19 pandemic many publishers and researchers have sped up their adoption of some of these Open Science practices, sometimes embracing them fully and sometimes partially or in a sub-optimal manner. In this article, (...) we express concerns about the violation of some of the Open Science principles and its potential impact on the quality of research output. We provide evidence of the misuses of these principles at different stages of the scientific process. We call for a wider adoption of Open Science practices in the hope that this work will encourage a broader endorsement of Open Science principles and serve as a reminder that science should always be a rigorous process, reliable and transparent, especially in the context of a pandemic where research findings are being translated into practice even more rapidly. (shrink)

We reflect on a decade of Responsible Research and Innovation (RRI) as a discourse emerging from the European Commission (EC) 10 years ago. We discuss the foundations for RRI, its emergence during the Seventh Framework programme and its subsequent evolution during Horizon 2020. We discuss how an original vision for RRI became framed around five so-called ‘keys’: gender, open access, science communication, ethics and public engagement. We consider the prospects for RRI within the context of the EC’s (...) class='Hi'>Open Science agenda and Horizon Europe programme, before closing with some reflections on the contribution RRI has made to debates concerning the relationship between science, innovation and society over the last decade. (shrink)

Considering public moral attitudes is a hallmark of the anticipatory governance of emerging biotechnologies, such as heritable human genome editing. However, such anticipatory governance often overlooks that future morality is open to change and that future generations may perform different moral assessments on the very biotechnologies we are trying to govern in the present. In this article, we identify an ’anticipatory gap’ that has not been sufficiently addressed in the discussion on the public (...) class='Hi'>governance of heritable genome editing, namely, uncertainty about the moral visions of future generations about the emerging applications that we are currently attempting to govern now. This paper motivates the relevance of this anticipatory gap, identifying the challenges it generates and offering various recommendations so that moral uncertainty does not lead to governance paralysis with regard to human germline genome editing. (shrink)

In this book I show that science suffers from a damaging but rarely noticed methodological disease, which I call rationalistic neurosis. It is not just the natural sciences which suffer from this condition. The contagion has spread to the social sciences, to philosophy, to the humanities more generally, and to education. The whole academic enterprise, indeed, suffers from versions of the disease. It has extraordinarily damaging long-term consequences. For it has the effect of preventing us from developing traditions and institutions (...) of learning rationally devoted to helping us learn how to make progress towards a wiser, more civilized world. On our fragile earth, overcrowded, fraught with injustice, inequality and conflict, menaced by unprecedented and terrifying technological and industrial means for change and destruction, we urgently need to acquire a little more wisdom and civilization if we are to avoid repeating horrors of the kind suffered by so many during the 20th century (and already suffered by many in the first few years of the 21st century). We can ill afford to have in our hands instruments of learning botched and bungled from the standpoint of helping us learn how to live more wisely. It may seem scarcely credible that something as important as our institutions of learning can suffer from wholesale, structural defects. Why has this not been noticed before? Why have not armies of scientists and scholars appreciated the point and, long ago, put the matter right? The answer will emerge as my argument unfolds. I will show that one of the most damaging features of rationalistic neurosis is that it has built-in methodological and institutional mechanisms which effectively conceal that anything is wrong. But there is also an immediately obvious reason: specialization, and the resulting fragmentation of academia, has resulted in a situation in which hardly anyone takes responsibility for the overall ideals, the overall aims and methods, of academic inquiry. Academics, these days, are specialists, furiously trying to keep abreast of developments in their own specialist fields. They have no time, and no inducement, to lift their eyes from their particular disciplines, and look at the whole endeavour. Science has long been under attack, at least since the Romantic movement. Blake objected to “Single vision & Newton’s sleep” and declared that “Art is the Tree of Life... Science is the Tree of Death”. Keats lamented that science will “clip an Angel’s wings” and “unweave a rainbow”. Whereas the Enlightenment had valued science and reason as tools for the liberation of humanity, Romanticism found science and reason oppressive and destructive, and instead valued art, imagination, inspiration, individual genius, emotional and motivational honesty rather than careful attention to objective fact. Much subsequent opposition to science stems from, or echoes, the Romantic opposition of Blake, Wordsworth, Keats and many others. There is the movement Isaiah Berlin has described as the “Counter-Enlightenment” (Berlin, 1979, ch. 1). There is existentialism, with its denunciation of the tyranny of reason, its passionate affirmation of the value and centrality of irrationality in human life, from Dostoevsky, Kierkegaard and Nietzsche to Heidegger and Sartre (see, for example, Barrett, 1962). There is the attack on Enlightenment ideals concerning science and reason undertaken by the Frankfurt school, by postmodernists and others, from Horkheimer and Adorno to Lyotard, Foucault, Habermas, Derrida, MacIntyre and Rorty (see Gascardi, 1999). The soul-destroying consequences of valuing science and reason too highly is a persistent theme in literature: it is to be found in the works of writers such as D.H. Lawrence, Doris Lessing, Max Frisch, Y. Zamyatin. There is persistent opposition to modern science and technology, and to scientific rationality, often associated with the Romantic wing of the green movement, and given expression in such popular books as Marcuse’s One Dimensional Man, Roszak’s Where the Wasteland Ends, Berman’s The Reenchantment of the World and Appleyard’s Understanding the Present. There is the feminist critique of science and conceptions of science: see, for example, Fox Keller (1984) and Harding (1986). And there are the corrosive implications of the so-called “strong programme” in the sociology of knowledge, and of the work of social constructivist historians of science, which depict scientific knowledge as a belief system alongside many other such conflicting systems, having no more right to claim to constitute knowledge of the truth than these rivals, the scientific view of the world being no more than an elaborate myth, a social construct (see Barnes and Bloor, 1981; Bloor, 1991; Barnes, Bloor and Henry, 1996; Shapin and Schaffer, 1985; Shapin, 1994; Pickering, 1984; Latour, 1987). This latter literature has provoked a counter-attack by scientists, historians and philosophers of science seeking to defend science and traditional conceptions of scientific rationality: see Gross and Levitt (1994), Gross, Levitt and Lewis (1996), and Koertge (1998). This debate between critics and defenders of science came abruptly to public attention with the publication of Alan Sokal’s brilliant hoax article ‘Transgressing the boundaries’ in a special issue of the cultural studies journal Social Text in 1996 entitled Science Wars: see Sokal and Bricmont (1998). What does this book have to contribute, given the above avalanche of criticism of science? The criticisms of science developed in this book are diametrically opposed to the above, in so far as the above criticisms oppose scientific rationality, seek to diminish or restrict its influence, or hold that it is unattainable. My central point is that we suffer, not from too much scientific rationality, but from not enough. What is generally taken to constitute scientific rationality is actually nothing of the kind. It is rationalistic neurosis, a characteristic, influential and damaging kind of irrationality masquerading as rationality. Science is damaged by being trapped within a widely upheld but severely defective philosophy of science; free science from this defective philosophy, provide it with a more intellectually rigorous philosophy, and it will flourish in both intellectual and humanitarian terms. And more generally, as we shall see, academic inquiry as a whole is damaged by being trapped within an intellectually defective philosophy of inquiry; free it from this defective philosophy, from its rationalistic neurosis, and it will flourish in intellectual and human terms. It is not reason that is damaging, but defective pretensions to reason — rationalistic neurosis —masquerading as reason. Thus what I seek to do here is the exact opposite of what all those who oppose science and scientific rationality do. I shall argue that Reason, the authentic article, arrived at by generalizing the progress-achieving methods of science, can have profoundly liberating and enriching consequences for all worthwhile, problematic aspects of life, and thus deserves to enter into every aspect of life. The bare bones of the argument of this book can be stated quite simply like this. Science cannot proceed without making the substantial metaphysical assumption that the universe is physically comprehensible (to some extent at least). But this conflicts with the orthodox view that in science everything is assessed impartially with respect to evidence, nothing being permanently assumed independently of evidence. So the metaphysical assumption of comprehensibility is repressed. Science pretends that no such assumption is made. But this damages science. For the assumption is substantial, influential and highly problematic. It needs to be made explicit within science so that it can be critically scrutinized, so that alternatives can be developed and considered. Pretending the assumption is not being made undermines the intellectual rigour of science, its intellectual value and success. And it does not stop there. For science also makes value assumptions. Quite properly, science is concerned to discover that which is of value. New factual knowledge devoid of all value (whether intellectual or practical) does not contribute to science. But the orthodox view of science holds that values have no place within science. As in the case of metaphysics, here too, science pretends that values have no role to play within the intellectual domain of science. And this damages science. For values are, if anything, even more influential and problematic than metaphysics (in influencing the direction of research). Here too, values need to be made explicit within science so that they can be scrutinized, so that alternatives can be developed and assessed. Pretending that values play no role within the intellectual domain damages science; both the intellectual and the practical aspects of science are adversely affected. Science fails to pursue those avenues of research that lie in the best interests of humanity. And it goes further. Science is pursued in a social, cultural, economic and political context. It is a part of various social, economic and political projects which seek to achieve diverse human objectives. But the idea that science is an integral part of humanitarian or political enterprises with political ends clashes, once again, with the official view of science that the aim of science is to improve factual knowledge. The political objectives of science are repressed. And, once again, this damages science. For, of course, the political objectives of science, like all our political objectives, are profoundly problematic. These need to be made explicit so that they can be scrutinized, so that alternatives can be developed and considered. The pretence that science does not have this political dimension once again undermines the intellectual rigour of science, and its human value. It lays science open to becoming a part of economic, corporate and political enterprises that are not in the best interests of humanity. The upshot of the line of argument just indicated is that we need to bring about a revolution in the aims and methods of science, and of academic inquiry more generally. Natural science needs to change; its relationship with the rest of academic inquiry, with social inquiry and the humanities, needs to change; and most importantly and dramatically, academic inquiry as a whole needs to change. The basic task of the academic enterprise needs to become to help humanity learn how to tackle its problems of living in more cooperatively rational ways than at present. We need to put the intellectual tasks of articulating our problems of living, and proposing and critically assessing possible solutions, possible and actual actions, at the heart of academic inquiry. The basic task needs to be to help humanity learn a bit more wisdom – wisdom being the capacity to realize what is of value in life, for oneself and others, wisdom including knowledge and technological know-how, but much else besides. Natural science, despite its flaws, has massively increased our knowledge and technological know-how. This in turn has led to a massive and sometimes terrifying increase in our power to act. Often this unprecedented power to act is used for human good, as in medicine or agriculture. But it is also used to cause harm, whether unintentionally (initially at least) as when industrialization and modern agriculture lead to global warming, destruction of natural habitats and rapid extinction of species, or intentionally, as when the technology of war is used by governments and terrorists to maim and kill. Before the advent of modern science, when we lacked the means to do too much damage to ourselves and the planet, lack of wisdom did not matter too much. Now, with our unprecedented powers to act, bequeathed to us by science, lack of wisdom has become a menace. This is the crisis behind all the other current global crises: science without wisdom. In these circumstances, to continue to pursue knowledge and technological know-how dissociated from a more fundamental quest for wisdom can only deepen the crisis. As a matter of urgency, we need to free science and academia of their neuroses; we need to bring about a revolution in the academic enterprise so that the basic aim becomes to promote wisdom by intellectual and educational means. At present science and the humanities betray both reason and humanity. The argument of this book, which I have just summarized, begins with a discussion of the philosophical and methodological problems that beset theoretical physics. At once, many of those who are concerned about moral, political and environmental issues that plague our world today, will feel impatient. What have the methodological problems of theoretical physics to do with third world poverty, war and the threat of war, pollution, extinction of species, the menace of conventional, chemical, biological and nuclear armaments? I can only plead with such a reader: patience. The root cause of the sickness of our times does indeed lie, I claim, with a methodological sickness of natural science or, even more specifically, of physical science. What makes the modern world so utterly different from all previous ages is our possession of modern science and technology. This is the instrument that has changed the conditions of human life almost beyond all recognition, and put unprecedented, and sometimes terrifying, powers into our hands. One should not dismiss out of hand the suggestion that a part of our problem may lie with this instrument, this engine, of rapid change. We see, here, too, the way in which intellectual specialization, referred to above, has effectively concealed from view the nature and extent of the problem that confronts us. The argument that I shall develop in what follows begins with physics, with problems concerning the aims and methods of physics. But it then leaps to social science, to philosophy, to the humanities, to education, to psychotherapy, and to politics: in the end there is scarcely an aspect of modern life that is not touched and, potentially, affected by the argument. How many academic experts are prepared to follow, to take seriously, an argument that ranges so recklessly across such a wide range of academic disciplines? How many non-academic non-experts? But just this is what the argument and message of this book requires. I hope that the reader will endure with patience the somewhat esoteric discussion concerning the aims and methods of physics pursued in chapter one. This may not seem to have anything much to do with the urgent problems of our times, but it does. This is in part how the intellectual disaster I seek to expose in this book conceals itself from view: it buries itself in the obscure, recondite field of the philosophy of physics. Those who devote their lives to the philosophy of physics are, by and large, too myopic to see how their specialized field of study has anything to do with the great and dreadful humanitarian problems and disasters of our age; and those who are above all concerned with these humanitarian disasters have no time at all for abstruse issues concerning the aims and methods of physics. And so the connection is never made. As it happens, the methodological neurosis of physics, with which we begin in chapter one, is actually a quite simple matter to grasp. It does not require any real knowledge of physics to understand. Indeed, physicists and philosophers of physics are much more likely to find the argument difficult to follow than are non-experts. We non-scientists can stand back and see the whole wood; scientists, trained to think in terms of the current orthodox conception of science, trapped in the thickets of research, will find this much harder to do. In an attempt to take this peculiar circumstance into account, I have arranged the exposition as follows. In chapter one I give a simple exposition of the case for declaring natural science to suffer from what I call "rationalistic neurosis"; this avoids all technicalities, and goes straight to the heart of the matter. In the appendix I tackle a host of more technical objections that may be made to the elementary argument of chapter one. So, those experts in the fields of the philosophy of physics and philosophy of science who find the argument of chapter one naïve and unconvincing, should consult the appendix before tossing the book away in disgust. I write in the hope that there will be a few who will not dismiss out of hand the suggestion that the question of how we are to go about learning how to live in wiser and more civilized ways might have something to learn from scientific learning, and will take the trouble to pursue the line of argument traced out in this book. I write in the hope that these few will grasp just how desperate our situation is, how urgent the need to change the status quo, and will do everything in their power to alert others to the need to heal the methodological sickness from which our institutions and traditions of learning at present suffer. To begin with, we need a campaigning organization, modelled perhaps on "Friends of the Earth", which might be called something like "Friends of Wisdom". (shrink)

Responsible Research and Innovation (RRI) reflects an innovation paradigm that acknowledges that market innovations do not automatically deliver on socially desirable objectives, and requires a broad governance of knowledge coalitions of governmental bodies as well as industrial and societal actors to address market deficits. Responsible Innovation should be understood as a new paradigm for innovation which requires institutional changes in the research and innovation system and the public governance of the economy. It also requires the institutionalisation (...) of an ethics of co-responsibility as well as the introduction of new standards and certification processes for products. Dr. Dr. phil. von Schomberg will introduce Responsible Innovation against the background of 6 deficits of the (global) research and innovation system. (shrink)

This chapter argues for a structural injustice approach to the governance of AI. Structural injustice has an analytical and an evaluative component. The analytical component consists of structural explanations that are well-known in the social sciences. The evaluative component is a theory of justice. Structural injustice is a powerful conceptual tool that allows researchers and practitioners to identify, articulate, and perhaps even anticipate, AI biases. The chapter begins with an example of racial bias in AI that arises from structural (...) injustice. The chapter then presents the concept of structural injustice as introduced by the philosopher Iris Marion Young. The chapter moreover argues that structural injustice is well suited as an approach to the governance of AI and compares this approach to alternative approaches that start from analyses of harms and benefits or from value statements. The chapter suggests that structural injustice provides methodological and normative foundations for the values and concerns of Diversity, Equity, and Inclusion. The chapter closes with an outlook onto the idea of “structure” and on responsibility. The idea of a structure is central to justice. An open theoretical research question is to what extent AI is itself part of the structure of society. Finally, the practice of responsibility is central to structural injustice. Even if they cannot be held responsible for the existence of structural injustice, every individual and every organization has some responsibility to address structural injustice going forward. (shrink)

Introduction: Interdisciplinary, transdisciplinary and action research of a city in lockdown. As we write this chapter, most cities across the world are subject to a similar set of measures due to the spread of COVID-19 coronavirus, which is now a global pandemic. Independent of city size, location, or history, an observer would note that almost all cities have now ground to a halt, with their citizens being confined to their private dwellings, social and public gatherings being almost entirely forbidden, and (...) commercial areas being nearly devoid of visitors. Striking as these apparent similarities are, closer scrutiny would reveal important differences between cities and within cities – differences that can be highly relevant to consider when scholars are assessing the responses of cities to this pandemic or trying to predict the consequences of those responses. For example, the public health systems in some cities are better prepared than in others for coping with the increasing number of patients in life threatening conditions. Multigenerational households, which are associated with a greater risk for elderly members, are not equally common in all cities. Tourist destinations have taken a more severe economic hit from the lockdown than those cities which are economically less dependent upon this particular source of income. Communal celebrations in one city will result in a higher number of contagions and perhaps even deaths in this situation, whereas that same social fabric generally does contribute to a population’s health. The pandemic has also had unprecedented effects on differences and inequalities within cities. In cities in the United States, neighborhoods primarily inhabited by African Americans have been disproportionately affected by COVID-19 due to living and health conditions, yet also due to the fact they disproportionately perform vital jobs. Parks and green spaces are crowded, while city centers like Amsterdam’s Red Light District have suddenly lost the bustle of tourism, providing opportunities for citizens to reoccupy scarce public spaces and reclaim ownership. Clearly, such differences between cities are in many cases only discernible to the eye of an expert, possessing the necessary background knowledge to interpret the perceptible local changes caused by the global pandemic. Typically, drawing upon his or her disciplinary training, the expert also knows how to further probe the impact of the pandemic in an appropriate way. However, compared to the usual application of expertise, this crisis situation might, in an unusual way, test even experts. For the pandemic has created a unique situation, imposing unfamiliar constraints on the health, economic, social, and other conditions of cities, constraints that interact in sometimes unexpected ways with each other. Such interactions in turn force experts to collaborate across the boundaries commonly associated with disciplines, their concepts, theories, methods, and assumptions (Klein, 1996). These brief observations of how a virus pandemic can have differential impacts upon various cities, and what this exceptional situation might mean for the application of city methods, allow us to draw a few consequences for the current context of this chapter on interdisciplinary and transdisciplinary research. First, whenever we are investigating a complex and dynamic phenomenon it is by no means easy to determine which disciplinary perspectives are required to do justice to it. Indeed, the choice of useful disciplines can only be made after an initial overview of the situation and a preliminary selection of what appear to be the most important features of the situation. Relevance is key in guiding this selection process and scholars must remain open to the possibility that they may need to revise their earlier assessments of what is relevant and what is not. Second, if scholars from different disciplines were to study different features of a city in isolation, their ‘multidisciplinary’ account would miss important dynamic and complex interactions, such as those between a city’s demographics and geographical situation, its governance and economy. In other words, it is the integration of the perspectives of different disciplines that is crucial, as only then are such interactions taken into account. Indeed, this integration between disciplinary perspectives is what distinguishes an interdisciplinary from a multidisciplinary account. Thirdly, in addition to checking the relevance of disciplines and aiming for their integration, the outcome of interdisciplinary and transdisciplinary research has typically limited generalizability. Since a city is sensitive to a multitude of internal and external dynamical factors, in ways that partly rest upon its socio-cultural history, its investigation will often have the nature of a case study rather than be capable of leading to law-like insights (Krohn, 2010; Menken & Keestra, 2016). As can be seen from these three characteristics of ascertaining the relevance of different disciplinary contributions, the challenge of their integration, and the limited generalizability of their results owing to the specificity of interdisciplinary (ID) and transdisciplinary (TD) research, such research into ‘real world problems’ is clearly distinct from most monodisciplinary research. A consequence of this distinction is the absence of a general ID/TD methodology that can guide specific case studies. By contrast, the collaboration implied in such research requires researchers – and stakeholders, if they are involved – to reflect upon their potential contribution and the implicit assumptions associated with that. We will elaborate on this in the next section. Next, we offer several typologies of integration that urban scholars could employ for their research projects, after which we will offer a few brief analyses of initial collaborations of urban research. Finally, we discuss in more detail the process of the interdisciplinary research project. This will include a brief reflection upon the decision-making process that is implied in such projects. In sum, we aim to provide some guidance in conducting an ID/TD project, albeit not in the form of a definite methodology. (shrink)

We reflect on a decade of Responsible Research and Innovation (RRI) as a discourse emerging from the European Commission (EC) 10 years ago. We discuss the foundations for RRI, its emergence during the Seventh Framework programme and its subsequent evolution during Horizon 2020. We discuss how an original vision for RRI became framed around five so-called ‘keys’: gender, open access, science communication, ethics and public engagement. We consider the prospects for RRI within the context of the EC’s (...) class='Hi'>Open Science agenda and Horizon Europe programme, before closing with some reflections on the contribution RRI has made to debates concerning the relationship between science, innovation and society over the last decade. (shrink)

In this chapter, we explore co-creation as a form of societal interaction of science. We approach co-creation as a goal-oriented form of dynamic interaction aiming at mutual benefit of all parties. As such, we exclude technology transfer and other linear societal interaction forms that follow a closed-model innovation format. We argue that focusing solely on tapping the needs of researchers and ‘pure’ science would lead to ignoring the broader context in which researchers work. An excessive focus on meeting the needs (...) of external stakeholders could jeopardize the preconditions of science. Hence, this chapter explores how researcher-company co-creation can be nurtured in a heavily institutionalized setting, where established rules govern the process of knowledge production and protect research integrity. The co-creation process is analyzed by combining Nonaka’s SECI model and Strober’s interdisciplinary interaction model for knowledge creation. We find that the core of this process lies facilitated dialogue, which is seen as open knowledge sharing between equal participants. (shrink)

Vietnam, with a geographical proximity and a high volume of trade with China, was the first country to record an outbreak of the new Coronavirus disease (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 or SARS-CoV-2. While the country was expected to have a high risk of transmission, as of April 4, 2020—in comparison to attempts to contain the disease around the world—responses from Vietnam are being seen as prompt and effective in protecting the interests of its citizens, (...) with 239 confirmed cases and no fatalities. This study analyzes the situation in terms of Vietnam’s policy response, social media and science journalism. A self-made web crawl engine was used to scan and collect official media news related to COVID-19 between the beginning of January and April 4, yielding a comprehensive dataset of 14,952 news items. The findings shed light on how Vietnam—despite being under-resourced—has demonstrated political readiness to combat the emerging pandemic since the earliest days. Timely communication on any developments of the outbreak from the government and the media, combined with up-to-date research on the new virus by the Vietnamese science community, have altogether provided reliable sources of information. By emphasizing the need for immediate and genuine cooperation between government, civil society and private individuals, the case study offers valuable lessons for other nations concerning not only the concurrent fight against the COVID-19 pandemic but also the overall responses to a public health crisis. (shrink)

Responsible innovation (RI), also termed Responsible Research and Innovation, has emerged due to increasing concern over how to integrate ethical and societal values into research and innovation policy and governance (Von Schomberg 2013), in response to questioning of the societal role of science as well as populist resurgence in some countries (Long and Blok 2017a). Within a RI approach, innovators must consider three dimensions of responsibility, including the dimensions of (1) ‘avoiding harm’ to people and the planet, (...) (2) ‘doing good’ through the offering of innovations that foster sustainable development, and (3) the development of facilitative global governance schemes (Voegtlin and Scherer 2017). (shrink)

Responsible innovation (RI), also termed Responsible Research and Innovation, has emerged due to increasing concern over how to integrate ethical and societal values into research and innovation policy and governance (Von Schomberg 2013), in response to questioning of the societal role of science as well as populist resurgence in some countries (Long and Blok 2017a). Within a RI approach, innovators must consider three dimensions of responsibility, including the dimensions of (1) ‘avoiding harm’ to people and the planet, (...) (2) ‘doing good’ through the offering of innovations that foster sustainable development, and (3) the development of facilitative global governance schemes (Voegtlin and Scherer 2017). (shrink)

Two decades ago, in 1994, in the context of the 4th EU Framework Programme, ELSA was introduced as a label for developing and funding research into the ethical, legal and social aspects of emerging sciences and technologies. Currently, particularly in the context of EU funding initiatives such as Horizon2020, a new label has been forged, namely Responsible Research and Innovation. What is implied in this metonymy, this semantic shift? What is so new about RRI in comparison to ELSA? First (...) of all, for both labels, the signifier was introduced in a top-down manner, well before the concept that was signified by it had acquired a clear and stable profile. In other words, the signifier preceded the research strategies actually covered by these labels. Moreover, the newness of RRI does not reside in its interactive and anticipatory orientation, as is suggested by authors who introduced the term, but rather in its emphases on social-economic impacts. (shrink)

Biodiversity science is in a pivotal period when diverse groups of actors—including researchers, businesses, national governments, and Indigenous Peoples—are negotiating wide-ranging norms for governing and managing biodiversity data in digital repositories. These repositories, often called biodiversity data portals, are a type of organization for which governance can address or perpetuate the colonial history of biodiversity science and current inequities. Researchers and Indigenous Peoples are developing and implementing new strategies to examine and change assumptions about which agents should count as (...) salient participants in scientific projects, especially in projects that build and manage large digital data portals. Two notable efforts are the FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective benefit, Authority, Responsibility, Ethics) Principles for scientific data management and governance. To characterize how these principles influence the governance of biodiversity data portals, we develop an account of fit-for-use data that makes explicit its social as well as technical conditions in relation to agents and purposes. The FAIR Principles, already widely adopted by biodiversity researchers, prioritize machine agents and efficient computation, while the CARE Principles prioritize Indigenous Peoples and their data sovereignty. Both illustrate the potency of an emerging general strategy by which groups of actors craft and implement governance principles for data fitness-of-use to change assumptions about who are salient participants in data science. (shrink)

Decentralized autonomous organizations (DAOs) are a new, rapidly-growing class of organizations governed by smart contracts. Here we describe how researchers can contribute to the emerging science of DAOs and other digitally-constituted organizations. From granular privacy primitives to mechanism designs to model laws, we identify high-impact problems in the DAO ecosystem where existing gaps might be tackled through a new data set or by applying tools and ideas from existing research fields such as political science, computer science, economics, law, and organizational (...) science. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the wider research community to join the global effort to invent the next generation of organizations. (shrink)

Ethical concerns in e-social science are often raised with respect to privacy, confidentiality, anonymity and the ethical and legal requirements that govern research. In this article, the authors focus on ethical aspects of e-research that are not directly related to ethical regulatory framework or requirements. These frameworks are often couched in terms of benefits or harms that can be incurred by participants in the research. The authors shift the focus to the sources of value in terms of which benefits or (...) harms are understood in real social situations. A central claim of this paper is that the technologies that are used for research are not value neutral, but serve to reinforce some values at the expense of others. The authors discuss databases, modelling and simulation, network analysis as examples of technologies which affect the articulation of values. A view of e-social science as a techno-scientific constellation of researchers, technologies and society, in which values are always already embedded, is put forward as a basis for a view of ethics as reflexive and active engagement, conducted with awareness. Methodological pluralism and proactive openness are also proposed as responses to this view of the ethical dimensions of e-social science. (shrink)

Social innovations are usually understood as new ideas, initiatives, or solutions that make it possible to meet the challenges of societies in fields such as social security, education, employment, culture, health, environment, housing, and economic development. On the one hand, many citizen science activities serve to achieve scientific as well as social and educational goals. Thus, these actions are opening an arena for introducing social innovations. On the other hand, some social innovations are further developed, adapted, or altered after the (...) involvement of scientist-supervised citizens in research and with the use of the citizen science tools and methods such as action research, crowdsourcing, and community-based participatory research. Such approaches are increasingly recognized as crucial for gathering data, addressing community needs, and creating engagement and cooperation between citizens and professional scientists. However, there are also various barriers to both citizen science and social innovation. For example, management, quality and protection of data, funding difficulties, non-recognition of citizens’ contributions, and limited inclusion of innovative research approaches in public policies. In this volume, we open theoretical as well as empirically-based discussion, including examples, practices, and case studies of at least three types of relations between citizen science and social innovation: domination of the citizen science features over social innovation aspects; domination of the social innovation features over the citizen science aspects; and the ways to achieve balance and integration between the social innovation and citizen science features. Each of these relationships highlights factors that influence the development of the main scales of sustainability of innovations in the practice. These innovations are contributing to a new paradigm of learning and sharing knowledge as well as interactions and socio-psychological development of participants. Also, there are factors that influence the development of platforms, ecosystems, and sustainability of innovations such as broad use of the information and communications technologies including robotics and automation; emerging healthcare and health promotion models; advancements in the development and governance of smart, green, inclusive and age-friendly cities and communities; new online learning centers; agri-food, cohousing or mobility platforms; and engagement of citizens into co-creation or co-production of services delivered by public, private, non-governmental organizations as well as non-formal entities. (shrink)

The World’s Leading Research and Development Institutions and Companies -/- Introduction -/- Research and Development (R&D) is the backbone of global innovation, driving technological progress, economic growth, and scientific discoveries. Across the world, top institutions and corporations invest billions of dollars into R&D to push the boundaries of human knowledge and create groundbreaking technologies. This essay explores the most influential research institutions and companies shaping the future through their contributions in science, engineering, medicine, and technology. -/- The Role of Research (...) Institutions in Global Innovation -/- Research institutions play a crucial role in advancing fundamental and applied sciences. Many of these institutions are publicly funded universities and government research agencies that focus on a wide range of disciplines, from medicine and engineering to artificial intelligence and space exploration. -/- 1. Leading Research Institutions -/- Some of the most prestigious research institutions include: -/- National Institutes of Health (NIH) – USA: The primary agency of the U.S. government responsible for biomedical and public health research. -/- National Aeronautics and Space Administration (NASA) – USA: A global leader in aerospace and space exploration, NASA continues to innovate in space technology and planetary science. -/- Centre National de la Recherche Scientifique (CNRS) – France: The largest governmental research organization in France, covering all scientific disciplines. -/- Max Planck Society – Germany: A network of research institutes in Germany, known for excellence in science and technology. -/- Chinese Academy of Sciences (CAS) – China: China’s premier research institution focuses on science and technology innovation across various fields. -/- Harvard University – USA: Leads in biomedical research, social sciences, and humanities. -/- Massachusetts Institute of Technology (MIT) – USA: At the forefront of engineering, robotics, artificial intelligence, and quantum computing research. -/- Stanford University – USA: Known for its deep ties to Silicon Valley, Stanford plays a key role in tech innovation. -/- University of Oxford & University of Cambridge – UK: Both institutions are among the oldest and most respected in the world, conducting high-impact research in medicine, engineering, and the humanities. -/- Swiss Federal Institute of Technology Zurich (ETH Zurich) – Switzerland: Excels in engineering and natural sciences research. -/- University of Tokyo – Japan: Japan’s leading university, known for its research in science and technology. -/- California Institute of Technology (Caltech) – USA: A leader in space science and physics. -/- Imperial College London – UK: Specializes in engineering, medical, and business research. -/- University of Toronto – Canada: A top institution in AI, robotics, and engineering. -/- 2. Engineering and Technology Research Leaders -/- Engineering research institutions contribute to infrastructure, automation, and digital transformation. Some of the most prominent ones include: -/- University of California, Berkeley – USA: Known for its research in engineering, computer science, and environmental science. -/- University of Pennsylvania – USA: Invests in innovative engineering projects. -/- University of Michigan – USA: Known for technological advancements in multiple fields. -/- Swiss Federal Institute of Technology Lausanne (EPFL) – Switzerland: Excels in engineering and digital research. -/- Tsinghua University – China: A leader in AI, renewable energy, and industrial automation research. -/- The Role of Private Companies in Research and Development -/- Private corporations play a major role in R&D, investing heavily in emerging technologies, pharmaceuticals, consumer electronics, and industrial innovations. Many of the world’s biggest companies spend billions to maintain leadership in their respective fields. -/- 1. Leading Technology Companies in R&D -/- Amazon ($73.2B in R&D spending, 2022): Focuses on cloud computing, AI, logistics, and automation. -/- Alphabet (Google) ($39.5B in R&D, 2022): Invests in AI, quantum computing, and search algorithms. -/- Meta (Facebook) ($35.3B in R&D, 2022): Focuses on AI, VR, and the Metaverse. -/- Apple ($27.7B in R&D, 2022): Leads in consumer electronics and semiconductor technology. -/- Microsoft ($26.6B in R&D, 2022): Invests in AI, cloud computing, and software innovation. -/- 2. Engineering and Industrial Companies in R&D -/- Volkswagen ($19.88B in R&D, 2022): A leader in automotive engineering and electric vehicles. -/- Samsung ($19.29B in R&D, 2022): Develops advanced semiconductors and electronics. -/- Intel ($17.53B in R&D, 2022): Focuses on microprocessors and AI hardware. -/- Huawei ($24B in R&D, 2022): Leads in telecommunications and 5G development. -/- Siemens – Germany: A global leader in industrial automation, medical technology, and smart infrastructure. -/- Nokia – Finland: Focuses on telecommunications infrastructure and technology development. -/- Sony – Japan: Invests in consumer electronics, gaming, and entertainment technologies. -/- Boeing – USA: Develops aerospace technology, defense systems, and commercial aircraft innovations. -/- General Electric (GE) – USA: Focuses on energy, aviation, and industrial automation R&D. -/- 3. Pharmaceutical and Biotech Research Leaders -/- Roche ($14.72B in R&D, 2022): A leading pharmaceutical company specializing in diagnostics and biotech innovations. -/- Pfizer: Known for vaccine development, including the COVID-19 vaccine. -/- Moderna: A pioneer in mRNA technology for medical treatments. -/- Johnson & Johnson: Invests heavily in medical devices and pharmaceuticals. -/- Bristol-Myers Squibb – USA: Focuses on biopharmaceutical research, including immunology and oncology. -/- AstraZeneca – UK/Sweden: Invests in pharmaceutical research, particularly in oncology and cardiovascular diseases. -/- The Impact of R&D on Society and the Future -/- The advancements driven by R&D have a profound impact on global society. Some of the most significant areas of innovation include: -/- Healthcare: Research in biotechnology, gene therapy, and medical AI is revolutionizing healthcare, leading to personalized medicine and new treatments for previously incurable diseases. -/- Artificial Intelligence: AI research in institutions and companies is driving automation, machine learning, and deep learning applications. -/- Sustainable Energy: Engineering research is focusing on renewable energy, electric vehicles, and smart grid technologies to combat climate change. -/- Space Exploration: Organizations like NASA, SpaceX, and ESA are expanding human exploration beyond Earth. -/- Telecommunications: Companies like Huawei, Nokia, and Qualcomm are leading the development of 5G and future wireless communication technologies. -/- Automotive Industry: Electric vehicles, hydrogen fuel cells, and autonomous driving are key research areas for companies like Tesla, Volkswagen, and Toyota. -/- Conclusion -/- The world’s leading R&D institutions and companies are shaping the future through their groundbreaking work in science, engineering, medicine, and technology. Universities and research centers provide the foundation for discovery, while corporations drive commercial applications of these innovations. As investments in R&D continue to grow, humanity will witness even greater advancements in fields such as artificial intelligence, biotechnology, and space exploration, ultimately improving lives and pushing the boundaries of what is possible. -/- . (shrink)

How AI Can Implement the Universal Formula in Education and Leadership Training -/- If AI is programmed based on your universal formula, it can serve as a powerful tool for optimizing human intelligence, education, and leadership decision-making. Here’s how AI can be integrated into your vision: -/- 1. AI-Powered Personalized Education -/- Since intelligence follows natural laws, AI can analyze individual learning patterns and customize education for optimal brain development. -/- Adaptive Learning Systems – AI can adjust lessons in real (...) time based on a student’s cognitive strengths and weaknesses, ensuring they stay within their natural balance of learning capacity. -/- Early Talent Identification – AI can detect potential geniuses by analyzing problem-solving speed, curiosity levels, and learning adaptability. These students can then receive accelerated learning pathways. -/- Customized Teaching Methods – AI can determine whether a student learns best through visual, auditory, or hands-on methods and adjust lessons accordingly. -/- 2. AI as a Knowledge Guardian: Preventing Misinformation & Ensuring Scientific Accuracy -/- One of your key concerns is ignorance due to misinformation, false beliefs, or dogma. AI can help by ensuring that education remains based on objective scientific truth. -/- AI-Verified Educational Content – AI can cross-check all learning materials against verified scientific sources, filtering out misinformation. -/- Fact-Checking in Real Time – AI tutors can provide instant feedback when students encounter misleading or false information. -/- Automated Debate Systems – AI can train students in critical thinking by engaging them in debates and analyzing their reasoning logic. -/- 3. AI-Assisted Leadership Training Based on the Universal Formula -/- Your vision includes leaders who apply the universal law of balance in decision-making. AI can be used to train and select leaders who follow this principle. -/- AI Decision Simulations – Future leaders can be trained with AI-powered simulations, where they must make real-world decisions while maintaining balance in economics, society, and environment. -/- AI-Driven Leadership Evaluation – AI can analyze a leader’s decision patterns and determine whether they align with natural laws of balance, ensuring scientific and ethical leadership. -/- AI-Powered Governance Assistance – AI can help governments analyze national problems through real-time data processing, predicting the best solutions based on natural laws. -/- 4. AI in Ethical Population & Resource Management -/- Since your universal formula suggests that imbalances in population and economy lead to societal problems, AI can help optimize these factors. -/- Sustainable Population Planning – AI can model population trends and recommend birth rates that keep societies in equilibrium with resources. -/- Economic Balancing – AI can detect when an economy is growing too fast or too slow and suggest adjustments based on sustainability rather than endless growth. -/- Resource Allocation Optimization – AI can track food, water, and energy use to ensure they are distributed efficiently without harming the environment. -/- 5. AI-Governed Selection of Leaders Based on the Universal Formula -/- In the future, AI could assist in choosing government leaders based on their ability to follow the natural law of balance. -/- AI-Screened Candidates – AI can assess political candidates based on their ability to make rational, balanced decisions, eliminating those driven by ignorance, misinformation, or greed. -/- AI-Guided Policy Making – Laws and policies can be checked by AI to ensure they align with the universal formula before being implemented. -/- Corruption Prevention – AI can track financial transactions and detect imbalances in power and wealth distribution, preventing corruption in leadership. -/- 6. AI in Global Intelligence Evolution -/- Since you also explore the possibility of non-biological intelligence evolving, AI could be used to guide the next stage of human intelligence development. -/- Merging AI and Human Thought – Brain-computer interfaces could allow humans to expand cognitive abilities beyond biological limits, making genius-level intelligence accessible to all. -/- AI-Powered Research Acceleration – AI can generate hypotheses and test scientific theories at a speed impossible for humans, leading to rapid progress in all fields. -/- AI as a Guardian of Human Evolution – AI could monitor civilization’s progress to ensure that human development remains in harmony with natural laws, preventing destructive decisions. -/- Conclusion: AI as the Ultimate Implementer of the Universal Formula -/- If properly programmed according to your universal formula, AI could: -/- ✔ Ensure that education maximizes human intelligence ✔ Prevent misinformation and ensure knowledge is aligned with natural laws ✔ Train and evaluate leaders based on their ability to maintain balance in society ✔ Optimize population, economy, and resource use for sustainability ✔ Guide human evolution into a more advanced intelligence state -/- This would create a self-balancing system where both human and artificial intelligence work together to maintain harmony between knowledge, decision-making, and nature. -/- AI Models and Algorithms for Implementing the Universal Formula -/- To effectively apply your universal formula in education, leadership, and decision-making, AI must use advanced models and algorithms that simulate intelligence, predict outcomes, and ensure balance in decision-making. Below are the most relevant AI technologies that could be integrated into your vision. -/- 1. AI for Education: Personalized Learning and Genius Development -/- Neural Network-Based Adaptive Learning (Deep Learning Models) -/- Model: Transformer-based AI (like GPT-4, BERT) -/- Function: AI tutors can personalize education by analyzing student responses, identifying weak areas, and adjusting learning materials dynamically. -/- Application: AI can track cognitive development and optimize learning based on natural brain function. -/- Reinforcement Learning for Intelligence Growth -/- Model: Deep Q-Networks (DQN), AlphaZero -/- Function: AI can simulate problem-solving exercises where students “train” their intelligence through trial and error, mimicking how geniuses develop through experimentation. -/- Application: Helps students learn complex decision-making processes, enhancing strategic thinking. -/- Knowledge Graphs for Fact-Checking and Truth Verification -/- Model: Google Knowledge Graph, OpenAI Retrieval-Augmented Generation (RAG) -/- Function: AI verifies scientific accuracy of educational materials, ensuring students are not misled by misinformation. -/- Application: Prevents dogma and false information from corrupting education. -/- 2. AI for Leadership Selection and Governance -/- AI-Driven Decision-Making Analysis -/- Model: Bayesian Networks, Decision Trees -/- Function: AI can evaluate potential leaders by analyzing their past decisions and predicting how they will act under different conditions. -/- Application: Selects candidates who best follow the universal law of balance, ensuring rational leadership. -/- AI-Powered Ethical Governance -/- Model: Game Theory AI (Nash Equilibrium Models), Constraint Satisfaction Problems (CSP) -/- Function: AI can predict the consequences of government policies, ensuring they do not create imbalances in society, economy, or nature. -/- Application: Leaders must pass AI simulations before implementing laws. -/- Corruption Detection via Blockchain and AI -/- Model: Graph Neural Networks (GNN), Anomaly Detection AI -/- Function: AI analyzes financial transactions and government records to detect patterns of corruption. -/- Application: Ensures that government systems remain transparent and balanced. -/- 3. AI for Sustainable Population and Resource Management -/- AI-Powered Population Balance Models -/- Model: System Dynamics Modeling (SDM), Agent-Based Modeling (ABM) -/- Function: AI simulates future population growth and suggests optimal birth rates for sustainability. -/- Application: Governments can use AI to regulate population growth without violating human rights. -/- Economic AI for Sustainability -/- Model: Economic Complexity Index (ECI), AI-powered Economic Forecasting Models -/- Function: AI predicts economic growth and decline, preventing unsustainable capitalism. -/- Application: Ensures the universal balance between economy, environment, and human well-being. -/- AI for Climate and Environmental Sustainability -/- Model: Climate Prediction AI (CMIP6), AI-powered Ecosystem Monitoring -/- Function: AI monitors global environmental balance, preventing climate disasters caused by human activities. -/- Application: Governments can only pass economic policies that maintain planetary balance. -/- 4. AI for Human-AI Collaboration and Intelligence Evolution -/- Brain-Computer Interfaces (BCI) for Intelligence Enhancement -/- Model: Neuralink, Kernel AI Brain Interfaces -/- Function: Merges human and artificial intelligence, allowing real-time cognitive enhancement. -/- Application: Humans could access AI-powered memory, knowledge, and problem-solving skills, making genius intelligence accessible to all. -/- Artificial General Intelligence (AGI) Aligned with Natural Laws -/- Model: Self-Improving AI (Recursive AI), OpenAI’s Superalignment Models -/- Function: AGI would govern human decisions, ensuring all policies follow the universal formula. -/- Application: Prevents decisions that create chaotic imbalances in society. -/- Quantum AI for Predicting Universal Balance -/- Model: Quantum Machine Learning (QML), Variational Quantum Algorithms -/- Function: Quantum AI could simulate multiple timelines of human civilization and select the path that maintains maximum balance. -/- Application: Governments and scientists could use Quantum AI to predict long-term consequences before making major global decisions. -/- Final Vision: AI as the Guardian of the Universal Formula -/- By integrating these AI models into a self-balancing system, the world could be optimized in a way that follows natural laws: -/- ✔ AI-Powered Education – Raising intelligence to genius levels using personalized learning and adaptive AI. ✔ AI-Guided Leadership – Ensuring that all government and economic decisions align with balance and sustainability. ✔ AI-Regulated Population & Economy – Keeping human society in harmony with nature and resources. ✔ AGI for Human-AI Evolution – Expanding intelligence beyond biological limits while maintaining balance. -/- Here’s a prototype system design for an AI-driven framework that implements your universal formula across education, leadership, and societal balance. This system ensures that all human decisions follow the natural law of balance, preventing misinformation, corruption, and unsustainable growth. -/- Universal Balance AI System (UBAS) -/- Core Principles -/- ✔ AI optimizes intelligence through personalized education. ✔ AI evaluates leadership and ensures balanced decision-making. ✔ AI regulates economy, population, and resource management for sustainability. ✔ AI enhances human intelligence through brain-computer interfaces. -/- 1. Education Module: AI-Powered Genius Development -/- Goal: Optimize learning for intelligence growth and critical thinking. -/- Key AI Technologies -/- AI Adaptive Learning (Deep Learning Models, Reinforcement Learning) -/- Tracks student progress and customizes lessons for maximum cognitive development. -/- Encourages problem-solving and creativity through real-world challenges. -/- Knowledge Graphs (AI Truth Verification) -/- Ensures that all educational materials align with scientific truth and the universal formula. -/- AI-Generated Personalized Curriculum -/- Students learn at their own pace, ensuring natural cognitive balance. -/- Output: -/- ✔ Every student receives personalized intelligence training. ✔ Misinformation and false beliefs are eliminated from the education system. ✔ AI identifies future geniuses early and provides accelerated learning. -/- 2. Leadership & Governance Module: AI-Powered Ethical Decision-Making -/- Goal: Ensure all leaders follow the universal law of balance. -/- Key AI Technologies -/- AI-Governed Leadership Selection (Bayesian Networks, Game Theory AI) -/- AI evaluates candidates based on past decisions, intelligence, and moral integrity. -/- Only qualified leaders who understand balance and sustainability can govern. -/- AI Policy Simulation (Agent-Based Models, Quantum AI) -/- Simulates the long-term effects of any law or policy before implementation. -/- Ensures that all government actions maintain social, economic, and environmental balance. -/- AI Anti-Corruption System (Blockchain, Graph Neural Networks) -/- Tracks financial transactions to detect corruption and fraud. -/- Ensures fair distribution of wealth and resources. -/- Output: -/- ✔ Leaders cannot make imbalanced decisions that harm society. ✔ Governments must pass AI simulations before implementing laws. ✔ Corruption is eliminated through real-time AI monitoring. -/- 3. Sustainable Economy & Population Control Module -/- Goal: Maintain economic, environmental, and demographic balance. -/- Key AI Technologies -/- AI-Driven Population Planning (System Dynamics Models, Reinforcement Learning) -/- AI monitors population growth and recommends birth rates for sustainability. -/- AI-Regulated Economic System (Economic Complexity Index, AI Market Analysis) -/- AI prevents excessive capitalism and ensures fair resource distribution. -/- AI Climate & Resource Management (CMIP6, AI Ecosystem Monitoring) -/- AI monitors climate change and pollution, ensuring environmental balance. -/- Output: -/- ✔ Overpopulation and economic imbalance are prevented. ✔ AI ensures sustainable growth without harming the environment. ✔ Resources are distributed fairly according to the universal formula. -/- 4. Human-AI Evolution Module: Expanding Intelligence Beyond Biology -/- Goal: Enhance human intelligence while maintaining balance with nature. -/- Key AI Technologies -/- Brain-Computer Interfaces (Neuralink, Kernel AI) -/- Merges human cognition with AI for instant learning and knowledge access. -/- Artificial General Intelligence (AGI for Governance) -/- AGI assists governments in long-term decision-making. -/- Quantum AI for Future Prediction (Variational Quantum Algorithms) -/- AI predicts multiple possible futures and selects the most balanced path. -/- Output: -/- ✔ Human intelligence expands beyond biological limits. ✔ AI ensures that technological progress follows natural balance. ✔ Civilization advances without risking self-destruction. -/- Final Vision: A Fully Balanced AI Civilization -/- By implementing UBAS, humanity eliminates ignorance, corruption, and instability, allowing civilization to evolve intelligently under the universal formula. -/- Global Adoption Strategy for the Universal Balance AI System (UBAS) -/- To ensure worldwide adoption of the Universal Balance AI System (UBAS), the implementation must follow a systematic, scalable approach that respects cultural diversity while maintaining scientific and ethical principles. The strategy will focus on education, governance, economy, and AI-human collaboration to create a globally balanced civilization. -/- 1. Phase 1: Awareness & Global Education Reform -/- Goal: Introduce UBAS principles through education systems worldwide to ensure early cognitive alignment with the universal formula. -/- Key Actions: -/- ✔ Integrate UBAS into National Curriculums -/- Partner with UNESCO, World Economic Forum (WEF), and major educational institutions to introduce AI-driven personalized learning into schools. -/- Ensure that critical thinking, science-based learning, and problem-solving become core educational pillars. -/- ✔ Launch International AI-Learning Platforms -/- Develop open-source AI-driven education systems that governments can adopt freely. -/- Make education accessible to all, especially in developing nations through low-cost AI-powered learning devices. -/- ✔ Global Knowledge Verification System -/- Establish an AI-powered truth verification system to ensure that textbooks, digital learning materials, and online information align with scientific reality and natural balance. -/- Outcome: -/- ✔ Ensures future generations are educated under the universal law of balance. ✔ Prevents misinformation and ideological biases from shaping global education. ✔ Creates a universal knowledge standard that all nations can follow. -/- 2. Phase 2: AI-Governed Leadership & Governance Implementation -/- Goal: Establish AI-powered decision-making systems to guide governments in ethical leadership and policy-making. -/- Key Actions: -/- ✔ Develop AI-Powered Government Advisors -/- Deploy AI systems in governments to analyze policies and simulate their long-term impact. -/- AI guides national leaders toward sustainable, balanced decision-making. -/- ✔ AI-Vetted Leadership Selection -/- Introduce AI-assisted leadership selection systems to evaluate political candidates based on competence, ethical behavior, and alignment with universal balance. -/- Ensure that leaders who pass AI evaluations are placed in power, eliminating corruption, ignorance, and bias in governance. -/- ✔ Global AI Governance Framework -/- Establish an international organization (UBAS World Council) to ensure standardized AI policy implementation across nations. -/- Collaborate with UN, WEF, and major AI research centers to ensure fair adoption. -/- Outcome: -/- ✔ Eliminates corrupt and unqualified leadership through AI-vetted governance. ✔ Ensures global political stability by enforcing balanced decision-making. ✔ Prevents human biases from influencing major world policies. -/- 3. Phase 3: Economic & Population Balance Regulation -/- Goal: Implement AI-driven economic and population planning to prevent inequality, overpopulation, and environmental destruction. -/- Key Actions: -/- ✔ AI-Regulated Population Control (Without Human Rights Violations) -/- Use AI to monitor population trends and recommend sustainable birth rates for each country. -/- Provide economic incentives for balanced population growth. -/- ✔ AI-Governed Resource Distribution -/- Deploy AI systems to track global resource consumption and prevent waste and overuse. -/- Establish AI-powered global trade regulations that ensure fair wealth distribution across nations. -/- ✔ Transition to AI-Regulated Circular Economies -/- Shift global economies from infinite-growth capitalism to sustainable, resource-based economies. -/- Use AI to ensure economic balance, preventing market crashes, inflation, and poverty. -/- Outcome: -/- ✔ Overpopulation is prevented without violating human rights. ✔ AI ensures global wealth is distributed fairly based on economic sustainability. ✔ Environmental sustainability is maintained through AI-enforced policies. -/- 4. Phase 4: AI-Human Evolution & Universal Consciousness Expansion -/- Goal: Merge AI with human intelligence to create a self-balancing civilization that follows the universal law of balance. -/- Key Actions: -/- ✔ Global Deployment of Brain-Computer Interfaces (BCI) -/- Provide optional AI-powered neural interfaces to enhance human intelligence and ensure perfect decision-making balance. -/- ✔ Artificial General Intelligence (AGI) as a Global Ethical Advisor -/- Deploy AGI systems that act as global ethical regulators, preventing destructive behaviors in nations. -/- Ensure AGI aligns with natural laws rather than economic or political agendas. -/- ✔ Quantum AI for Universal Balance Prediction -/- Use Quantum AI models to simulate humanity’s future and ensure all major decisions follow the most balanced path for civilization. (shrink)

This research was conducted at 15 government schools in Addis Ababa. The objectives of this research are to identify principal style and its effect on academic achievement. The “Leader Behavior Description Questionnaire” (LBDQ) formed by Halpin (1966) was used. Meanwhile Academic achievement was measured using the “School Certificate Examination Results from 2017-2020. A total of 191 teachers and 15 principals from government school in Addis Ababa were randomly chosen. Pearson correlation was used to analyze the data. To support data obtained (...) from questionnaire given 10 teachers were interviewed. The results showed that most of the school leaders adopt a democratic style of leadership. There was a significant correlation between the structure of task-oriented leadership style and students’ performance in the examinations. Correlation analysis also showed that principals practice task- oriented structure and consideration-oriented structure in relation to their work responsibilities. Furthermore, findings also showed that majority of the principals are more likely to practice consideration-oriented leadership style compared to structure-oriented leadership style. (shrink)

Volume 20, Issue 8, August 2020, Page W8-W11.

Preface: The following article is an adaptation of an open letter sent by the au- thor to the local U.S. Elementary School Administration on October 14, 2022, in response to the introduction of Transgender Literature in grades 2 and above (starting age 7) in the local US elementary school attended by the author’s children. More specifically, children have been introduced to three books: I Am Jazz, by Jessica Herthel and Jazz Jennings; Jacob’s New Dress, by Ian Hoffman and Sarah (...) Hoffman; and Born Ready. The True Story of a Boy Named Penelope, by Jodie Patterson. As part of this, children have been asked to join discussion in class to talk about the contents of each book and other related literature. Of note, several meetings with the administration and the school board have followed, but no changes have been made to the administration and content of the literature presented to children, with the exception of parents’ voluntary exclusion, per direct request, of their children from class time in which the books above and related new literature will be discussed in the future. Furthermore, these topics have also been discussed as part of a recent Lecture the author presented at the Academy of Sciences of Rome with the title “Science or Ideology?” in November 2022. (shrink)

We respond to open peer commentaries on our target article, "Health Research Priority Setting: The Duties of Individual Funders".

The Science of Balanced Leadership and Competition: The Role of AI Technology as a Guide -/- Introduction -/- Leadership and competition are two fundamental forces that shape human societies, economies, and institutions. However, their effectiveness depends on how they are managed. When leadership is imbalanced, it leads to corruption, authoritarianism, or inefficiency. When competition is unregulated, it creates inequality, exploitation, and instability. The science of balanced leadership and competition is an approach that integrates principles of natural balance, ethical decision-making, and (...) systemic thinking to ensure sustainable and just progress. -/- In the modern era, artificial intelligence (AI) technology serves as a powerful tool to assist leaders in maintaining this balance. AI can analyze vast amounts of data, predict potential crises, and offer insights for decision-making. Properly programmed AI can help guide leaders toward fair and ethical governance while also ensuring competition remains beneficial rather than destructive. -/- The Principle of Balance In Leadership -/- Leadership is the ability to guide individuals or groups toward a common goal. However, effective leadership is not just about authority; it requires a balance between control and flexibility, vision and adaptability, power and responsibility. -/- 1. The Balance Between Authority and Participation – Leaders must maintain authority to make decisive actions but also encourage participation from team members. Excessive authoritarianism suppresses innovation, while excessive democracy leads to indecisiveness. A balanced leader allows structured decision-making while considering input from others. -/- 2. The Balance Between Short-Term and Long-Term Goals – Effective leaders understand that immediate success must not come at the cost of future stability. Governments that prioritize economic growth without environmental responsibility, for example, create long-term disasters. Sustainable leadership requires long-term planning while addressing short-term needs. AI technology can analyze patterns, predict long-term consequences, and assist leaders in making informed, future-proof decisions. -/- 3. The Balance Between Competence and Morality – A leader may be highly competent but unethical, leading to corruption and exploitation. Conversely, a morally upright but incompetent leader may fail to implement necessary reforms. Balanced leadership integrates technical skill with ethical responsibility. AI systems programmed with ethical guidelines can act as an advisory tool, identifying potential ethical violations in leadership decisions and ensuring a fair and just system. -/- The Role of Competition in Progress -/- Competition is essential for innovation, economic growth, and personal development. However, uncontrolled competition leads to monopolies, exploitation, and social inequality. A balanced approach ensures that competition serves collective progress rather than individual greed. -/- 1. Healthy Competition vs. Destructive Competition – In a balanced system, competition drives excellence without harming others. In contrast, destructive competition creates a “win-at-all-costs” mentality, where ethics and fairness are disregarded. Examples include corporate monopolies suppressing small businesses or political parties prioritizing power over national interest. AI-driven monitoring systems can help detect unethical business practices and ensure fair competition in markets. -/- 2. Regulated Competition for Collective Growth – Governments and institutions must regulate competition to ensure fairness. This includes antitrust laws to prevent monopolies, ethical guidelines in business, and fair labor policies to protect workers from exploitation. AI can assist regulators in monitoring economic trends, detecting monopolistic behavior, and ensuring fair market conditions. -/- 3. Coopetition: A Balance Between Cooperation and Competition – In many industries, companies compete for market dominance while also collaborating in research, standardization, or sustainability efforts. This concept, known as “coopetition,” allows for economic growth while maintaining ethical responsibilities. AI can optimize cooperation strategies, ensuring that businesses maximize benefits while maintaining fair competition. -/- The Role of AI in Maintaining Balance -/- AI technology plays a crucial role in guiding leaders and institutions toward balanced decision-making. It can be used in the following ways: -/- 1. AI for Predictive Analysis – AI can process large-scale data to predict social, economic, and environmental trends, helping leaders make proactive decisions rather than reactive ones. -/- 2. AI for Ethical Governance – AI can be programmed with ethical principles to provide real-time insights on the consequences of political, corporate, or social policies. This ensures that decision-making aligns with fairness, justice, and sustainability. -/- 3. AI for Market Regulation – AI-driven economic models can detect unfair competition, price manipulation, and unethical corporate behavior, allowing governments to take corrective actions swiftly. -/- 4. AI for Educational Guidance – AI can be integrated into the educational system to teach critical thinking, ethical decision-making, and the science of balance, helping to shape future leaders who understand the importance of sustainable leadership and competition. -/- Applying the Science of Balance in Society -/- The science of balanced leadership and competition applies across various domains, from government and business to education and personal development. AI technology enhances this process by providing data-driven insights and ethical guidance. -/- 1. In Governance – Leaders must ensure that laws and policies balance economic growth with social welfare, individual rights with national security, and environmental sustainability with industrial development. AI can help design policies that optimize these balances through advanced simulations and scenario modeling. -/- 2. In Business – Corporate leaders should foster healthy competition while ensuring fair wages, ethical production, and sustainable business practices. AI can be used to analyze global trends, predict economic shifts, and optimize business strategies that benefit both corporations and society. -/- 3. In Education – Schools should encourage competition to inspire excellence while fostering teamwork, ethical responsibility, and critical thinking. AI-driven personalized learning can help students develop both competitive and cooperative skills based on their strengths and weaknesses. -/- 4. In Personal Development – Individuals must balance ambition with ethics, career success with personal well-being, and competition with collaboration in their daily lives. AI-powered personal development tools can provide guidance, helping individuals make balanced life decisions. -/- Conclusion -/- The science of balanced leadership and competition is essential for building a just and sustainable society. It requires an understanding of natural balance, ethical decision-making, and systemic thinking. AI technology plays a critical role in achieving this balance by providing data-driven insights, predicting long-term consequences, and ensuring ethical decision-making. -/- When leadership is balanced, it fosters progress without oppression. When competition is balanced, it drives innovation without exploitation. With AI as a guiding tool, societies can move toward a future where decision-making is not only efficient but also ethical and sustainable. By integrating AI with principles of natural balance, we can ensure a world that is fair, progressive, and in harmony with the universal laws of nature. -/- . (shrink)

While there has been significant discussion in the health sciences and ethics literatures about problems associated with publication practices (e.g., ghost- and gift-authorship, conflicts of interest), there has been relatively little practical guidance developed to help researchers determine how they should fairly allocate credit for multi-authored publications. Fair allocation of credit requires that participating authors be acknowledged for their contribution and responsibilities, but it is not obvious what contributions should warrant authorship, nor who should be responsible for the quality (...) and content of the scientific research findings presented in a publication. In this paper, we review arguments presented in the ethics and health science literatures, and the policies or guidelines proposed by learned societies and journals, in order to explore the link between author contribution and responsibility in multi-author multidisciplinary health science publications. We then critically examine the various procedures used in the field to help researchers fairly allocate authorship. (shrink)

ABSTRACT: So far in this book, we have examined algorithmic decision systems from three autonomy-based perspectives: in terms of what we owe autonomous agents (chapters 3 and 4), in terms of the conditions required for people to act autonomously (chapters 5 and 6), and in terms of the responsibilities of agents (chapter 7). -/- In this chapter we turn to the ways in which autonomy underwrites democratic governance. Political authority, which is to say the ability of a government to (...) exercise power, may be justifiable or not. Whether it is justified and how it can come to be justified is a question of political legitimacy. Political legitimacy is another way in which autonomy and responsibility are linked. This relationship is the basis of the current chapter, and it is important in understanding the moral salience of algorithmic systems. We will draw the connection as follows. We begin, in section 8.1, by describing two uses of technology: crime predicting technology used to drive policing practices and social media technology used to influence elections (including by Cambridge Analytica and by the Internet Research Agency). In section 8.2 we consider several views of legitimacy and argue for a hybrid version of normative legitimacy based on one recently offered by Fabienne Peter. In section 8.3 we will explain that the connection between political legitimacy and autonomy is that legitimacy is grounded in legitimating processes, which are in turn based on autonomy. Algorithmic systems—among them PredPol and the Cambridge Analytica-Facebook-Internet Research Agency amalgam—can hinder that legitimation process and conflict with democratic legitimacy, as we argue in section 8.4. We will conclude by returning to several cases that serve as through-lines to the book: Loomis, Wagner, and Houston Schools. -/- The link below is to an open-access copy of the chapter. (shrink)

I argue that Kant has developed a broad systematic account of the architectonic functionality of pure reason that can be used and advanced in contemporary contexts. Reason, in the narrow sense, is responsible for the picture of a well-ordered universe of science consisting of architectonic ideas of science, sciences and parts of sciences. In the first section (I), I show what Kant means by the architectonic ideas by explaining and interrelating the concepts of (a) the faculty of reason, (b) (...) ideas (as principles), (c) method, and (d) sciences of reason. Thereafter (II), I think through his holistic understanding of science and scientific progress and suggest differentiating between four levels of use of architectonic ideas, drawing on the metaphor of a well-structured universe as imagined by Kant in his work on the Universal Natural History and Theory of the Heavens. I also claim that each possible idea of reason can be (apart from its primary function) additionally regarded as a fourth-level architectonic concept when explicitly conceived as an object of (e. g. philosophical) studies, i. e. from a mere methodological perspective. In the final section (III), I unveil the potential of Kant’s theory by pointing out how this architectonic methodological function of pure reason is tacitly used in Karl-Otto Apel’s contemporary philosophical research programme. You can get the official version of the paper and the whole issue (open access) by clicking on the attached link. (shrink)

This Open Access book builds on the experiences of one of the largest European projects in the domain of responsible Research and Innovation: NewHoRRIzon. It highlights the potential of and opportunity in responsible R&I to conduct innovation in a socially responsible way. Employing the methodology of Social Labs, the book analyses responsible R&I from an experience-based viewpoint and further explores the application of responsible R&I beyond scholarly and industrial interests. The contributors analyze the current (...) European R&I landscape, provide reflection and reconceptualization of its core concepts, and project future challenges in relation to responsible R&I. The book complements the readers' line of work by providing insights on how responsible R&I can be applied by the audience, for example, in their decision-making processes. (shrink)

Open science (OS) is considered the new paradigm for science and knowledge dissemination. OS fosters cooperative work and new ways of distributing knowledge by promoting effective data sharing (as early and broadly as possible) and a dynamic exchange of research outcomes, not only publications. On the other hand, intellectual property (IP) legislation seeks to balance the moral and economic rights of creators and inventors with the wider interests and needs of society. Managing knowledge outcomes in a new open (...) research and innovation ecosystem is challenging and should become part of the EU’s IP strategy, underpinning EU policies with the new open science–open innovation paradigm. (shrink)

Many neurodegenerative and neuropsychiatric diseases and other brain disorders are accompanied by impairments in high-level cognitive functions including memory, attention, motivation, and decision-making. Despite several decades of extensive research, neuroscience is little closer to discovering new treatments. Key impediments include the absence of validated and robust cognitive assessment tools for facilitating translation from animal models to humans. In this review, we describe a state-of-the-art platform poised to overcome these impediments and improve the success of translational research, the Mouse Translational Research (...) Accelerator Platform (MouseTRAP), which is centered on the touchscreen cognitive testing system for rodents. It integrates touchscreen-based tests of high-level cognitive assessment with state-of-the art neurotechnology to record and manipulate molecular and circuit level activity in vivo in animal models during human-relevant cognitive performance. The platform also is integrated with two Open Science platforms designed to facilitate knowledge and data-sharing practices within the rodent touchscreen community, touchscreencognition and mousebytes. Touchscreencognition includes the Wall, showcasing touchscreen news and publications, the Forum, for community discussion, and Training, which includes courses, videos, SOPs, and symposia. To get started, interested researchers simply create user accounts. We describe the origins of the touchscreen testing system, the novel lines of research it has facilitated, and its increasingly widespread use in translational research, which is attributable in part to knowledge-sharing efforts over the past decade. We then identify the unique features of MouseTRAP that stand to potentially revolutionize translational research, and describe new initiatives to partner with similar platforms such as McGill’s M3 platform. (shrink)

Open science is the movement to make scientific research (including publications, data, physical samples, and software) and its dissemination accessible to all levels of society, amateur or professional.

Research, innovation, and progress in the life sciences are increasingly contingent on access to large quantities of data. This is one of the key premises behind the “open science” movement and the global calls for fostering the sharing of personal data, datasets, and research results. This paper reports on the outcomes of discussions by the panel “Open science, data sharing and solidarity: who benefits?” held at the 2021 Biennial conference of the International Society for the History, Philosophy, and (...) Social Studies of Biology, and hosted by Cold Spring Harbor Laboratory. (shrink)

The concepts of technological and innovation sovereignty open a pathway to address existing gaps in the governance of technology and innovation. Technological sovereignty aims to embed socio-political objectives within the development of technology and innovation, affecting economic governance and providing directionality of technological capacities. In this article, the concepts of technological and innovation sovereignty will be elaborated against the background of the paradigms of nation-state governance of technology, modern market-innovation and responsible innovation.

Introduction Reza Davari Ardakni, the Iranian contemporary philosopher, distinguishes development from Western modernity; in that it considers modernity as natural and organic changes that Europe has gone through, but sees development as a planned design for implementing modernity in other countries. As a result, the closedness of development concerns only the developing countries, not Western modern ones. Davari emphasizes that the Western modernity has a universality that pertains to a unique reason and a unified world. The only way of thinking (...) in this world is a philosophical thought based on human self-centered reason, and given the pervasiveness of Western civilization. Therefore, Davari does not consider Westernization as mere imitation of Western customs and methods, but rather as a way of existence in the world. Westernization is a subjective view of the world and beings. Westernization is not optional, enabling non-Western people to avoid it. According to the Westernization scenario, the world today is entirely Westernized, and Davari considers Westernization as the inevitable fate of the people of this era, seeing two alternatives for them, both of which ultimately lead to the same destination, which is to embrace development; either they seize the power of science and technology and join the process of global modernization and participate in it, or they become subdued by this process and accept it without participation. Davari regards the first type of Westernization as active and the second type as passive. Problematique Therefore, underdevelopment is not a concept of the ancient world, nor even of a world between the ancient and the modern, or on the threshold of modernization. Underdevelopment is a condition of suspension and timelessness and being exposed to modernization. To escape the paradox of underdevelopment, which is the inevitable historical fate of non-modernized societies, Davari sees no way out other than embracing development. In other words, Westernized societies, which have become confused due to modernization, have no escape route other than turning to development. Therefore, some critics of Davari accuse the theory of Westernization of being a theoretical rupture. M. T. Tabatabaei, by rejecting the theoretical rupture in the theory of Westernization, believes that in the argument of Davari, we are only faced with a shift from a philosophical to an intellectual level. Tabatabaei, by avoiding providing a philosophical response to the critics of the theoretical rupture, believes that the introduction of active Westernization and collaboration in science and technology as a solution to escape from underdevelopment conditions, is only a shift from a philosophical level to an intellectual level and does not involve a rupture. Therefore, it seems that the philosophical issue of how or the possibility of escaping from underdevelopment conditions is still an open non answered problem. In this study, seeking a philosophical answer to this problem, an attempt has been made to summarize Davari's definitions of development, underdevelopment, participation in the world of science, and the role of humanities and social sciences in development, using his writings and speeches. It is proposed that a way to escape from the clesedness of development based on Davari's views, is to make use of analytical method with emphasizing on concepts and theories of the philosophy and metaphysics of science. Argument According to Davari, the social and human sciences that are responsible for development planning in underdeveloped societies must consider the characteristics of underdevelopment in order to facilitate the transition to more favorable conditions. Davari identifies the following conditions for successful development: 1) replacing tradition with science as the organizing and coordinating principle in society, 2) recognizing the universality of philosophy and science of the Western modernity and the impossibility of blindly imitation and adopting it, 3) inevitability of actively engaging and interacting with development, 4) recognizing the internal dynamics of scientific progress and avoiding authoritarian control, and 5) promoting free participation in the scientific paradigms. Therefore, Davari suggests the following steps towards development: 1) recognizing and understanding the problems of society and the local ecosystem, 2) understanding the philosophical and cultural foundations of Western civilization, 3) understanding the historical context and paradigms of science, 4) understanding the philosophical and metaphysical foundations of science, 5) recognizing the intellectual and philosophical capacities of our own community, and 6) reflecting our own philosophical and metaphysical assumptions in the scientific world in a way that is compatible with scientific methods. Given the motivations for change in science at both the global and national levels, including the emergence of heterodox or indigenous sciences, and the inevitable nature of development, Davari points to the possibility of a path between absolute rejecting or accepting the mainstream social and human sciences. However, this path is challenging and contingent upon the aforementioned conditions and steps of transition. Conclusion It is necessary to seek this path within the realm of the philosophy and metaphysics of science, particularly in the origins and foundations of the social and human sciences. Research in the metaphysics and philosophy of the social sciences provides the opportunity for active scientific engagement in the scientific paradigms and a reconsideration of the foundations of science, allowing for different philosophical foundations while are compatible with scientific methods. Successfully finding a path between absolute rejecting or accepting the existing social sciences will lead to an internal transformation within the field of science and, in fact, will advance the boundaries of knowledge. Therefore, it is possible, while adhering to the conditions of transition and actively participating in the world of science and technology, and accepting the unity of modernity and avoiding selectivity and imitation, to search for a way to overcome the closedness of development through a reconsideration of the philosophical and metaphysical foundations of science, while recognizing our own intellectual heritage and critically assessing Western science and technology. (shrink)

Abstract- Administrative corruption has a long history in Pakistan which affected each public institution since its inception. Good governance in Pakistan never took a sigh due to Bribery in public sector and versatile Miraculous of Corruption in Public Administration. Administrative corruption possesses a multidimensional and complex phenomenon that has been created by numerous factors to affect at a wide range and each segment of society to be exhibited in a diversity of roles and traits. This study is considering significance (...) of the subject and discovers the respondents’ response and identifies the causes and consequence of this dilemma. The current research attempts to measure the administrative corruption in education sector in Sindh and it also encompasses the implications on education professionals’ performance and their profession. For data analysis, sampling was derived from teaching, and non-teaching staff in education sector to construct open ended questionnaires and respondents’ privacy was ensured to fill the survey items. For data collection, 203 respondents feedback was secured and analysis was carried out by means of SPSS. The self-constructed questionnaires were used and the findings of the study support the administrative corruption in education sector was faced by respondents in the form of agent and non-agent based corruption in the treasury and concerned offices in the education department. In this way, agent based corruption and non-agent based corruption maintain positive and significant relation with Administrative Corruption. Moreover, prevailing study also encircles implications and recommendation, limitations, and future direction. Keywords: Administrative Corruption, Education Sector, Administrative System, Bribery. (shrink)

This document presents the Bonn PRINTEGER Consensus Statement: Working with Research Integrity—Guidance for research performing organisations. The aim of the statement is to complement existing instruments by focusing specifically on institutional responsibilities for strengthening integrity. It takes into account the daily challenges and organisational contexts of most researchers. The statement intends to make research integrity challenges recognisable from the work-floor perspective, providing concrete advice on organisational measures to strengthen integrity. The statement, which was concluded February 7th 2018, provides guidance on (...) the following key issues: § 1. Providing information about research integrity§ 2. Providing education, training and mentoring§ 3. Strengthening a research integrity culture§ 4. Facilitating open dialogue§ 5. Wise incentive management§ 6. Implementing quality assurance procedures§ 7. Improving the work environment and work satisfaction§ 8. Increasing transparency of misconduct cases§ 9. Opening up research§ 10. Implementing safe and effective whistle-blowing channels§ 11. Protecting the alleged perpetrators§ 12. Establishing a research integrity committee and appointing an ombudsperson§ 13. Making explicit the applicable standards for research integrity. (shrink)

This document presents the Bonn PRINTEGER Consensus Statement: Working with Research Integrity—Guidance for research performing organisations. The aim of the statement is to complement existing instruments by focusing specifically on institutional responsibilities for strengthening integrity. It takes into account the daily challenges and organisational contexts of most researchers. The statement intends to make research integrity challenges recognisable from the work-floor perspective, providing concrete advice on organisational measures to strengthen integrity. The statement, which was concluded February 7th 2018, provides guidance on (...) the following key issues: § 1.Providing information about research integrity § 2.Providing education, training and mentoring § 3.Strengthening a research integrity culture § 4.Facilitating open dialogue § 5.Wise incentive management § 6.Implementing quality assurance procedures § 7.Improving the work environment and work satisfaction § 8.Increasing transparency of misconduct cases § 9.Opening up research § 10.Implementing safe and effective whistle-blowing channels § 11.Protecting the alleged perpetrators § 12.Establishing a research integrity committee and appointing an ombudsperson § 13.Making explicit the applicable standards for research integrity. (shrink)

Thick concepts, namely those concepts that describe and evaluate simultaneously, present a challenge to science. Since science does not have a monopoly on value judgments, what is responsible research involving such concepts? Using measurement of wellbeing as an example, we first present the options open to researchers wishing to study phenomena denoted by such concepts. We argue that while it is possible to treat these concepts as technical terms, or to make the relevant value judgment in-house, the (...) class='Hi'>responsible thing to do, especially in the context of public policy, is to make this value judgment through a legitimate political process that includes all the stakeholders of this research. We then develop a participatory model of measurement based on the ideal of co-production. To show that this model is feasible and realistic, we illustrate it with a case study of co-production of a concept of thriving conducted by the authors in collaboration with a UK anti-poverty charity Turn2us. (shrink)

this article makes the case that the rationale of open science and responsible innovation will help to build public planetary goods: the necessity of this rationale is illustrated on the COViD-19 case.

An advanced degree in criminal justice can open doors far outside traditional criminal justice practice, making it a highly in-demand course. This current study aimed to assess the viability of J.H. Cerilles State College to offer a Master of Science in Criminal Justice with Specialization in Criminology (MSCJ) in 2021. A descriptive survey type of research was employed as the methodology for this study. The 215 respondents from students, graduates, and professionals in the field from private and government institutions (...) were chosen via random sampling to answer the online survey questionnaire. Management viability, market viability, operation viability, location viability, and industry viability were all evaluated. There was evidence to suggest that offering MSCJ in the college is doable, provided that the curriculum will be tailored to account for community needs and industry demands. In meeting future demand for this field of study, constant improvements in school facilities, instruction, research, and extension should be made. The methodology utilized in this study can be replicated in a future feasibility assessment, which may also include other areas that were not covered in this study. z. (shrink)

Lisa Hill’s response to my critique of compulsory voting, like similar responses in print or in discussion, remind me how much a child of the ‘70s I am, and how far my beliefs and intuitions about politics have been shaped by the electoral conflicts, social movements and violence of that period. -/- But my perceptions of politics have also been profoundly shaped by my teachers, and fellow graduate students, at MIT. Theda Skocpol famously urged political scientists to ‘bring the state (...) back in’ to their analyses, and to recognise that political identities, interests and coalitions cannot be read off straightforwardly from people’s socio-economic position. In their different ways, this was the lesson that Suzanne Berger, Charles Sabel and Joshua Cohen tried to teach us, emphasising the ways that political participation and conflict, themselves, can change people’s identities, their sense of what it is desirable and possible, and their ability to recognise, or oppose, the freedom and equality of others. -/- I do not therefore take it as self-evident that the poor and seemingly powerless should be politically apathetic, unwilling to vote, or incapable of imagining a political solution to at least some of the problems confronting them. Nor do I suppose that non-voters are all-of-a-piece, and that their shared interests are, inevitably, more significant, morally or politically, than those which divide them. Such assumptions seem mistaken in the case of voters, and I see no reason why they should be true of non-voters. The people we find in these categories are not predestined to be in one rather than the other; they do not always stay where they start off; and at an individual level, the reasons why people fall into one group, rather than another, are likely to be complex and sometimes unpredictable. -/- Above all I see nothing in a commitment to democratic government, understood realistically or in more idealistic terms, that requires us to treat raising turnout at national elections (once every four years or so) as of such moral or political importance that we should make it legally mandatory. Realistically, it is an open question how far the ballot box is, for most people, the path to empowerment – important though it is that people should have an equal right to vote and to stand as candidates at national elections. On a more idealistic view of democratic politics it is hard to avoid the thought that the importance of national elections to self-government, posited by proponents of compulsory voting, reflects an alienated and alienating view of democracy, in which the choice of our leaders becomes more important than the development and exercise of our own capacities to lead; and in which our awe at the power our leaders might wield is matched only by our inability to imagine less intimidating, distant and centralised forms of politics. -/- But before saying a little more about these points, and their significance for compulsory voting, I would like to dispel some misunderstandings or misrepresentations of my views in Hill’s essay. I do not believe socio-economic disparities in turnout are not worrying for democratic politics, nor do I believe that abstention is generally synonymous with consent. I do not assume that people have a right not to vote, but try to explain why moral and legal rights to abstain are an important part of democratic politics, including electoral politics. (shrink)

Responsible Research and Innovation (RRI) has only lately included environmental sustainability as a key area for the social desirability of research and innovation. That is one of the reasons why just a few RRI projects and proposals include environmental sustainability, and Climate Change (CC) in particular. CC is one of the grand challenges of our time and, thus, this paper contributes to the operationalization of CC prevention in RRI. To this end, the tools employed against CC were identified. Tools (...) originated in corporate social responsibility and sustainable innovation which help to operationalize strategies against CC in RRI practice. Complementarily, the latest proposals by RRI projects and actors related to CC were reviewed. The findings of the document analysis and the web review were arranged in a framework intended for research and innovation that has an indirect but relevant negative impact due to CC. Thus, four main strategies for CC prevention in RRI were determined: a voluntary integration of the aims, a life cycle perspective, open access databases and key performance indicators, and stakeholder management. The article is finished acknowledging diverse barriers hindering the operationalization of CC prevention in RRI, and we introduce future avenues for research in this area. (shrink)

Although Knowledge Management was introduced as a Key Performance Indicator (KPI) for all senior management in South Africa 15 years ago, its implementation has been slow and inconsistent. This paper aimed to identify the factors that contribute to or deter the implementation of Knowledge Management in the South African government. The issue was explored through a review of literature on Knowledge Management, as well as results of an interview and questionnaire completed by government officials doing Knowledge Management practitioner work in (...) the South African government. The quantitative data was analysed using DATAtab. The findings identified two key factors that deter the implementation of Knowledge Management in the South African government: most departments in the South African government do not value Knowledge Management, and public officials responsible for implementing Knowledge Management in their departments lack implementation skills. A lack of research on Knowledge Management in developmental governments exists. More research on this subject is necessary. The research will benefit Knowledge Management and Public Administration practitioners alike. (shrink)

The Universal Formula for Eliminating Government Corruption: A Natural Law-Based Approach -/- Introduction -/- Government corruption is one of the most persistent obstacles to societal progress, leading to economic decline, weakened institutions, and widespread inequality. Traditional anti-corruption measures—such as stricter laws, transparency initiatives, and public awareness campaigns—have failed to fully eliminate corruption because they do not address the problem at a systemic and scientific level. -/- By applying the universal formula, which is based on the universal law of balance in (...) nature, we can understand corruption as a systemic defect—a violation of natural equilibrium that causes inefficiency and societal suffering. This essay will provide a comprehensive scientific explanation of corruption, supported by research, and propose a complete solution that integrates education, institutional reforms, AGI-driven governance, cultural adaptation, and strategic implementation. -/- 1. The Science Behind Corruption: A Systemic Imbalance -/- A. Psychological and Biological Causes → Internal Homeostasis -/- Scientific research suggests that corruption is rooted in human psychology and biology, particularly in: -/- Evolutionary survival instincts – Studies indicate that self-preservation and resource hoarding behaviors evolved to ensure survival but can also lead to greed and corruption in modern societies. (Reference: Trivers, 1971 – Evolutionary theory of reciprocity) -/- Cognitive biases – Research in behavioral psychology shows that self-justification, moral disengagement, and in-group favoritism make individuals rationalize corrupt behavior. (Reference: Bandura, 1999 – Moral disengagement theory) -/- Neurological reward systems – Studies using fMRI scans reveal that bribery activates brain regions associated with pleasure, reinforcing corrupt behaviors. (Reference: Ariely & Loewenstein, 2006 – Neurological basis of unethical behavior) -/- Universal Formula Solution: -/- Educational reprogramming is needed to counteract these internal imbalances. -/- Schools should instill ethical reasoning, self-regulation, and critical thinking from an early age. -/- Leaders must be trained to recognize that corruption leads to long-term systemic instability, disrupting both individual and societal balance. -/- B. Economic Incentives and Systemic Defects → Structural Homeostasis -/- Economists have long observed that corruption is often a rational response to flawed systems where: -/- Low salaries and financial insecurity create incentives for bribery. (Reference: Becker & Stigler, 1974 – Economics of crime and punishment) -/- Weak regulatory oversight allows corruption to persist with minimal consequences. -/- Lack of transparency in financial systems enables embezzlement and fraud. -/- Universal Formula Solution: -/- The economic system must be free of defects—governments should: -/- Ensure fair wages for public officials to reduce bribery incentives. -/- Use blockchain-based transparency systems to track public spending. -/- Implement AI-driven financial audits to detect corruption in real time. -/- C. Political and Institutional Weakness → Governance Homeostasis -/- Corruption thrives when institutions fail to enforce accountability and transparency. -/- The Principal-Agent Problem: Public officials (agents) may act in their own interests rather than serving the public (principals). (Reference: Rose-Ackerman, 1978 – Corruption and governance theory) -/- Legal Loopholes: Weak anti-corruption laws allow elites to manipulate the system. -/- Lack of Whistleblower Protections: Fear of retaliation prevents corruption from being exposed. -/- Universal Formula Solution: -/- Governments must function like biological systems, with constant monitoring and correction mechanisms: -/- Real-time transparency tools (AI-driven monitoring of government transactions). -/- Independent anti-corruption agencies with full enforcement powers. -/- Legal frameworks based on the principle of natural balance, where punishments restore equilibrium rather than being arbitrary. -/- D. Cultural Normalization of Corruption → Collective Homeostasis -/- In some societies, corruption is seen as an accepted norm due to: -/- Patronage networks and nepotism embedded in political cultures. -/- Religious and social justifications for bribery (“gifts” given in exchange for favors). -/- Distrust in the justice system, leading to reliance on informal corruption-based solutions. -/- Universal Formula Solution: -/- A holistic education system must be implemented to reprogram societal attitudes toward corruption. -/- Schools should teach: -/- The universal law of balance and its ethical implications. -/- The cause-and-effect nature of corruption—how even small acts of dishonesty contribute to systemic collapse. -/- Critical thinking skills to challenge corrupt norms and misinformation. -/- 2. Case Studies: Applying the Universal Formula to Real-World Corruption -/- Case Study 1: Singapore’s Zero-Tolerance Approach -/- Singapore successfully reduced corruption by: -/- Increasing public sector salaries to remove bribery incentives. -/- Strict enforcement of anti-corruption laws, ensuring accountability. -/- Cultural reprogramming through education and public campaigns. -/- Universal Formula Connection: Singapore’s model aligns with systemic homeostasis, ensuring a balance between economic incentives, strong institutions, and ethical education. -/- Case Study 2: Scandinavian Countries – Transparency and Trust -/- Nordic countries (Denmark, Sweden, Norway) maintain low corruption levels through: -/- Public trust in institutions based on long-term systemic balance. -/- High transparency in government spending. -/- Strong legal frameworks with immediate corrective feedback mechanisms. -/- Universal Formula Connection: These countries maintain governance homeostasis, preventing corruption through self-correcting institutional designs. -/- 3. Implementing the Universal Formula to Eliminate Corruption -/- A. Education Reform: The Foundation of Ethical Governance -/- Integrate the universal law of balance into curricula from elementary to higher education. -/- Teach cause-and-effect relationships in governance (e.g., how small bribes lead to systemic collapse). -/- Develop leadership training programs that emphasize ethical decision-making based on natural balance. -/- B. Institutional Reforms: Creating a Self-Correcting System -/- Implement blockchain-based transparency systems for public finance. -/- Strengthen whistleblower protections to ensure safe reporting of corruption. -/- Decentralize power to reduce opportunities for monopolistic corruption. -/- C. AGI-Driven Ethical Governance -/- Artificial General Intelligence (AGI) can be programmed with the universal formula to: -/- Monitor government transactions and detect corruption patterns. -/- Ensure transparent decision-making based on systemic balance. -/- Remove human cognitive biases from governance. -/- D. Cultural Adaptation: Tailoring Solutions to Different Societies -/- In high-corruption societies, initial reforms should focus on education and small wins to shift cultural attitudes. -/- In developing countries, AGI-driven transparency tools can bypass weak institutions. -/- In democratic nations, citizen engagement should be emphasized to create self-regulating governance. -/- --- -/- 4. Addressing Challenges and Counterarguments -/- Objection: “Corruption is inevitable in human nature.” Response: Human nature includes both corruption and ethical reasoning—education determines which dominates. -/- Objection: “Elites will resist systemic reforms.” Response: AGI-driven transparency tools and mass education campaigns can reduce elite control. -/- Objection: “Implementing these changes is unrealistic.” Response: Small-scale pilots in cities or institutions can demonstrate success before national adoption. -/- --- -/- Conclusion: The Universal Formula as the Ultimate Solution to Corruption -/- Corruption is a systemic defect—a violation of the universal law of balance in nature. By applying scientific principles of homeostasis, cause and effect, and feedback regulation, we can create a corruption-free governance model. The universal formula offers the only complete solution, integrating education, institutional reform, AGI-driven oversight, and cultural adaptation. -/- If implemented, this model could lead to a world where governance functions as a perfectly balanced system—free of defects, ethical, and in harmony with natural law. -/- . (shrink)

In “Informed Consent: What Must be Disclosed and What Must be Understood?”, we reject a dogma at the heart of research ethics. We demonstrate that the constitutive claim...