Current advances in research, development and application of artificial intelligence systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compares 22 guidelines, highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, (...) I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems—and how the effectiveness in the demands of AI ethics can be improved. (shrink)

This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other (...) stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society. (shrink)

Artificial Intelligence (AI) is already having a major impact on society. As a result, many organizations have launched a wide range of initiatives to establish ethical principles for the adoption of socially beneficial AI. Unfortunately, the sheer volume of proposed principles threatens to overwhelm and confuse. How might this problem of ‘principle proliferation’ be solved? In this paper, we report the results of a fine-grained analysis of several of the highest-profile sets of ethical principles for AI. We assess whether these (...) principles converge upon a set of agreed-upon principles, or diverge, with significant disagreement over what constitutes ‘ethical AI.’ Our analysis finds a high degree of overlap among the sets of principles we analyze. We then identify an overarching framework consisting of five core principles for ethical AI. Four of them are core principles commonly used in bioethics: beneficence, non-maleficence, autonomy, and justice. On the basis of our comparative analysis, we argue that a new principle is needed in addition: explicability, understood as incorporating both the epistemological sense of intelligibility (as an answer to the question ‘how does it work?’) and in the ethical sense of accountability (as an answer to the question: ‘who is responsible for the way it works?’). In the ensuing discussion, we note the limitations and assess the implications of this ethical framework for future efforts to create laws, rules, technical standards, and best practices for ethical AI in a wide range of contexts. (shrink)

The debate about the ethical implications of Artificial Intelligence dates from the 1960s :741–742, 1960; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by Neural Networks and Machine Learning techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles—the (...) ‘what’ of AI ethics —rather than on practices, the ‘how.’ Awareness of the potential issues is increasing at a fast rate, but the AI community’s ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs. (shrink)

This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: opacity as intentional corporate or state (...) secrecy, opacity as technical illiteracy, and an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices, and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm. (shrink)

Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016, 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative (...) concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms. (shrink)

Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016, 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative (...) concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms. (shrink)

Modern digital technologies—from web-based services to Artificial Intelligence (AI) solutions—increasingly affect the daily lives of billions of people. Such innovation brings huge opportunities, but also concerns about design, development, and deployment of digital technologies. This article identifies and discusses five clusters of risk in the international debate about digital ethics: ethics shopping; ethics bluewashing; ethics lobbying; ethics dumping; and ethics shirking.

Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as biased. While researchers have (...) taken the problem of algorithmic bias seriously, but the current discussion on algorithmic fairness tends to conceptualize ‘fairness’ in algorithmic fairness primarily as a technical issue and attempts to implement pre-existing ideas of ‘fairness’ into algorithms. In this paper, I show that such a view of algorithmic fairness as technical issue is unsatisfactory for the type of problem algorithmic fairness presents. Since decisions on fairness measure and the related techniques for algorithms essentially involve choices between competing values, ‘fairness’ in algorithmic fairness should be conceptualized first and foremost as a political issue, and it should be (re)solved by democratic communication. The aim of this paper, therefore, is to explicitly reconceptualize algorithmic fairness as a political question and suggest the current discussion of algorithmic fairness can be strengthened by adopting the accountability for reasonableness framework. (shrink)

In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence. In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a ‘good AI society’. To do so, we examine how each report addresses the following three topics: the development of a ‘good (...) AI society’; the role and responsibility of the government, the private sector, and the research community in pursuing such a development; and where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a ‘good AI society’. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach. (shrink)

As the range of potential uses for Artificial Intelligence, in particular machine learning, has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the theory of (...) AI ethics principles and the practical design of AI systems. In previous work, we analysed whether it is possible to close this gap between the ‘what’ and the ‘how’ of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice. We concluded that this method of closure is currently ineffective as almost all existing translational tools and methods are either too flexible or too strict. This raised the question: if, even with technical guidance, AI ethics is challenging to embed in the process of algorithmic design, is the entire pro-ethical design endeavour rendered futile? And, if no, then how can AI ethics be made useful for AI practitioners? This is the question we seek to address here by exploring why principles and technical translational tools are still needed even if they are limited, and how these limitations can be potentially overcome by providing theoretical grounding of a concept that has been termed ‘Ethics as a Service.’. (shrink)

People increasingly form beliefs based on information gained from automatically filtered Internet ‎sources such as search engines. However, the workings of such sources are often opaque, preventing ‎subjects from knowing whether the information provided is biased or incomplete. Users’ reliance on ‎Internet technologies whose modes of operation are concealed from them raises serious concerns about ‎the justificatory status of the beliefs they end up forming. Yet it is unclear how to address these concerns ‎within standard theories of knowledge and justification. (...) To shed light on the problem, we introduce a ‎novel conceptual framework that clarifies the relations between justified belief, epistemic responsibility, ‎action, and the technological resources available to a subject. We argue that justified belief is subject to ‎certain epistemic responsibilities that accompany the subject’s particular decision-taking circumstances, ‎and that one typical responsibility is to ascertain, so far as one can, whether the information upon which ‎the judgment will rest is biased or incomplete. What this responsibility comprises is partly determined by ‎the inquiry-enabling technologies available to the subject. We argue that a subject’s beliefs that are ‎formed based on Internet-filtered information are less justified than they would be if she either knew how ‎filtering worked or relied on additional sources, and that the subject may have the epistemic ‎responsibility to take measures to enhance the justificatory status of such beliefs.‎. (shrink)

Algorithms silently structure our lives. Algorithms can determine whether someone is hired, promoted, offered a loan, or provided housing as well as determine which political ads and news articles consumers see. Yet, the responsibility for algorithms in these important decisions is not clear. This article identifies whether developers have a responsibility for their algorithms later in use, what those firms are responsible for, and the normative grounding for that responsibility. I conceptualize algorithms as value-laden, rather than neutral, in that algorithms (...) create moral consequences, reinforce or undercut ethical principles, and enable or diminish stakeholder rights and dignity. In addition, algorithms are an important actor in ethical decisions and influence the delegation of roles and responsibilities within these decisions. As such, firms should be responsible not only for the value-laden-ness of an algorithm but also for designing who-does-what within the algorithmic decision. As such, firms developing algorithms are accountable for designing how large a role individual will be permitted to take in the subsequent algorithmic decision. Counter to current arguments, I find that if an algorithm is designed to preclude individuals from taking responsibility within a decision, then the designer of the algorithm should be held accountable for the ethical implications of the algorithm in use. (shrink)

Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To (...) achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, ‘SITL = HITL + Social Contract.’. (shrink)

According to Palazzo and Scherer, the changing role of business corporations in society requires that we take new measures to integrate these organizations into society-wide processes of democratic governance. We argue that their model of integration has a fundamental problem. Instead of treating business corporations as agents that must be held accountable to the democratic reasoning of affected parties, it treats corporations as agents who can hold others accountable. In our terminology, it treats business corporations as “supervising authorities” rather than (...) “functionaries.” The result is that Palazzo and Scherer’s model does not actually address the democratic deficit that it is meant to solve. In order to fix the problem, we advocate removing business corporations from any policymaking role in political CSR and limiting participation to political NGOs and other groups that meet the standards we set out for a politically representative organization (PRO). (shrink)

We are currently witnessing a sharp rise in the use of algorithmic decision-making tools. In these instances, a new wave of policy concerns is set forth. This article strives to map out these issues, separating the wheat from the chaff. It aims to provide policy makers and scholars with a comprehensive framework for approaching these thorny issues in their various capacities. To achieve this objective, this article focuses its attention on a general analytical framework, which will be applied to a (...) specific subset of the overall discussion. The analytical framework will reduce the discussion to two dimensions, every one of which addressing two central elements. These four factors call for a distinct discussion, which is at times absent in the existing literature. The two dimensions are the specific and novel problems the process assumedly generates and the specific attributes which exacerbate them. While the problems are articulated in a variety of ways, they most likely could be reduced to two broad categories: efficiency and fairness-based concerns. In the context of this discussion, such problems are usually linked to two salient attributes the algorithmic processes feature—its opaque and automated nature. (shrink)

ABSTRACT:I here advance a critical research agenda for the political perspective of corporate social responsibility (Political CSR). I argue that whilst the ‘Political’ CSR literature is notable for both its conceptual novelty and practical importance, its development has been hamstrung by four ambiguities, conflations and/or oversights. More positively, I argue that ‘Political’ CSR should be conceived as one potentialformof globalization, and not as aconsequenceof ‘globalization’; that contemporary Western MNCs should be presumed to engage in CSR for instrumental reasons; that ‘Political’ (...) CSR should be associated with a corresponding ‘political’ model of corporate governance; and that both a ‘Rawlsian’ and ‘Habermasian’ perspective of Political CSR are different from ‘Political’ CSR. In concluding, I use these four critiques to identify a number of areas within which increasingly robust and sophisticated positive and normative theories of Political CSR are required. (shrink)

A major approach to the ethics of artificial intelligence is to use social choice, in which the AI is designed to act according to the aggregate views of society. This is found in the AI ethics of “coherent extrapolated volition” and “bottom–up ethics”. This paper shows that the normative basis of AI social choice ethics is weak due to the fact that there is no one single aggregate ethical view of society. Instead, the design of social choice AI faces three (...) sets of decisions: standing, concerning whose ethics views are included; measurement, concerning how their views are identified; and aggregation, concerning how individual views are combined to a single view that will guide AI behavior. These decisions must be made up front in the initial AI design—designers cannot “let the AI figure it out”. Each set of decisions poses difficult ethical dilemmas with major consequences for AI behavior, with some decision options yielding pathological or even catastrophic results. Furthermore, non-social choice ethics face similar issues, such as whether to count future generations or the AI itself. These issues can be more important than the question of whether or not to use social choice ethics. Attention should focus on these issues, not on social choice. (shrink)

ABSTRACT:Many companies engage in dialogue with nongovernmental organizations about societal issues. The question is what a regulative ideal for such dialogues should be. In the literature on corporate social responsibility, the Habermasian notion of communicative action is often presented as a regulative ideal for stakeholder dialogue, implying that actors should aim at consensus and set strategic considerations aside. In this article, we argue that in many cases, communicative action is not a suitable regulative ideal for dialogue between companies and NGOs. (...) We contend that there is often an adversarial element in the relation between companies and NGOs, and that an orientation towards consensus can be in tension with this adversarial relation. We develop an alternative approach to stakeholder dialogue called ‘agonistic deliberation.’ In this approach, conflict and strategic considerations play a legitimate and, up to a certain point, desirable role. (shrink)

While organizations today make extensive use of complex algorithms, the notion of algorithmic accountability remains an elusive ideal due to the opacity and fluidity of algorithms. In this article, we develop a framework for managing algorithmic accountability that highlights three interrelated dimensions: reputational concerns, engagement strategies, and discourse principles. The framework clarifies that accountability processes for algorithms are driven by reputational concerns about the epistemic setup, opacity, and outcomes of algorithms; that the way in which organizations practically engage with emergent (...) expectations about algorithms may be manipulative, adaptive, or moral; and that when accountability relationships are heavily burdened by the opacity and fluidity of complex algorithmic systems, the emphasis of engagement should shift to a rational communication process through which a continuous and tentative assessment of the development, workings, and consequences of algorithms can be achieved over time. The degree to which such engagement is, in fact, rational can be assessed based on four discourse-ethical principles of participation, comprehension, multivocality, and responsiveness. We conclude that the framework may help organizations and their environments to jointly work toward greater accountability for complex algorithms. It may further help organizations in reputational positioning surrounding accountability issues. The discourse-ethical principles introduced in this article are meant to elevate these positioning contests to extend beyond mere adaption or compliance and help guide organizations to find moral and forward-looking solutions to accountability issues. (shrink)

Discursive deference in the governance of science and technology is rebalancing from expert analysis toward participatory deliberation. Linear, scientistic conceptions of innovation are giving ground to more plural, socially situated understandings. Yet, growing recognition of social agency in technology choice is countered by persistently deterministic notions of technological progress. This article addresses this increasingly stark disjuncture. Distinguishing between “appraisal” and “commitment” in technology choice, it highlights contrasting implications of normative, instrumental, and substantive imperatives in appraisal. Focusing on the role of (...) power, it identifies key commonalities transcending the analysis/participation dichotomy. Each is equally susceptible to instrumental framing for variously weak and strong forms of justification. To address the disjuncture, it is concluded that greater appreciation is required—in both analytic and participatory appraisal—to facilitating the opening up of governance commitments on science and technology. (shrink)

Transparency is now a fundamental principle for data processing under the General Data Protection Regulation. We explore what this requirement entails for artificial intelligence and automated decision-making systems. We address the topic of transparency in artificial intelligence by integrating legal, social, and ethical aspects. We first investigate the ratio legis of the transparency requirement in the General Data Protection Regulation and its ethical underpinnings, showing its focus on the provision of information and explanation. We then discuss the pitfalls with respect (...) to this requirement by focusing on the significance of contextual and performative factors in the implementation of transparency. We show that human–computer interaction and human-robot interaction literature do not provide clear results with respect to the benefits of transparency for users of artificial intelligence technologies due to the impact of a wide range of contextual factors, including performative aspects. We conclude by integrating the information- and explanation-based approach to transparency with the critical contextual approach, proposing that transparency as required by the General Data Protection Regulation in itself may be insufficient to achieve the positive goals associated with transparency. Instead, we propose to understand transparency relationally, where information provision is conceptualized as communication between technology providers and users, and where assessments of trustworthiness based on contextual factors mediate the value of transparency communications. This relational concept of transparency points to future research directions for the study of transparency in artificial intelligence systems and should be taken into account in policymaking. (shrink)

Earth’s life-support system is facing megaproblems of sustainability. One important way of how these problems can be addressed is through innovation. This paper argues that responsible innovation that contributes to sustainable development consists of three dimensions: innovations avoid harming people and the planet, innovations ‘do good’ by offering new products, services, or technologies that foster SD, and global governance schemes are in place that facilitate innovations that avoid harm and ‘do good.’ The paper discusses global governance schemes based on deliberation (...) as a means to foster such responsible innovation. These schemes can provide voluntary soft-law regulations that complement and extend national and international hard-law regulations and facilitate collective innovation that contributes to SD goals. The article addresses the facilitative role of governments and international organizations in overcoming problems of deliberation and offers illustrative examples of such governance schemes. (shrink)

Online service providers —such as AOL, Facebook, Google, Microsoft, and Twitter—significantly shape the informational environment and influence users’ experiences and interactions within it. There is a general agreement on the centrality of OSPs in information societies, but little consensus about what principles should shape their moral responsibilities and practices. In this article, we analyse the main contributions to the debate on the moral responsibilities of OSPs. By endorsing the method of the levels of abstract, we first analyse the moral responsibilities (...) of OSPs in the web. These concern the management of online information, which includes information filtering, Internet censorship, the circulation of harmful content, and the implementation and fostering of human rights. We then consider the moral responsibilities ascribed to OSPs on the web and focus on the existing legal regulation of access to users’ data. The overall analysis provides an overview of the current state of the debate and highlights two main results. First, topics related to OSPs’ public role—especially their gatekeeping function, their corporate social responsibilities, and their role in implementing and fostering human rights—have acquired increasing relevance in the specialised literature. Second, there is a lack of an ethical framework that can define OSPs’ responsibilities, and provide the fundamental sharable principles necessary to guide OSPs’ conduct within the multicultural and international context in which they operate. This article contributes to the ethical framework necessary to deal with and by endorsing a LoA enabling the definition of the responsibilities of OSPs with respect to the well-being of the infosphere and of the entities inhabiting it. (shrink)

Transparency is an increasingly prominent area of research that offers valuable insights for organizational studies. However, conceptualizations of transparency are rarely subject to critical scrutiny and thus their relevance remains unclear. In most accounts, transparency is associated with the sharing of information and the perceived quality of the information shared. This narrow focus on information and quality, however, overlooks the dynamics of organizational transparency. To provide a more structured conceptualization of organizational transparency, this article unpacks the assumptions that shape the (...) extant literature, with a focus on three dimensions: conceptualizations, conditions, and consequences. The contribution of the study is twofold: On a conceptual level, we provide a framework that articulates two paradigmatic positions underpinning discussions of transparency, verifiability approaches and performativity approaches; on an analytical level, we suggest a novel future research agenda for studying organizational transparency that pays attention to its dynamics, paradoxes, and performative characteristics. (shrink)

Data science, and the related field of big data, is an emerging discipline involving the analysis of data to solve problems and develop insights. This rapidly growing domain promises many benefits to both consumers and businesses. However, the use of big data analytics can also introduce many ethical concerns, stemming from, for example, the possible loss of privacy or the harming of a sub-category of the population via a classification algorithm. To help address these potential ethical challenges, this paper maps (...) and describes the main ethical themes that were identified via systematic literature review. It then identifies a possible structure to integrate these themes within a data science project, thus helping to provide some structure in the on-going debate with respect to the possible ethical situations that can arise when using data science analytics. (shrink)

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining and fairness, accountability and transparency machine learning, their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate emergent indirect discrimination-by-proxy, such (...) as redlining. Such organisations might also lack the knowledge and capacity to identify and manage fairness issues that are emergent properties of complex sociotechnical systems. This paper presents and discusses three potential approaches to deal with such knowledge and information deficits in the context of fairer machine learning. Trusted third parties could selectively store data necessary for performing discrimination discovery and incorporating fairness constraints into model-building in a privacy-preserving manner. Collaborative online platforms would allow diverse organisations to record, share and access contextual and experiential knowledge to promote fairness in machine learning systems. Finally, unsupervised learning and pedagogically interpretable algorithms might allow fairness hypotheses to be built for further selective testing and exploration. Real-world fairness challenges in machine learning are not abstract, constrained optimisation problems, but are institutionally and contextually grounded. Computational fairness tools are useful, but must be researched and developed in and with the messy contexts that will shape their deployment, rather than just for imagined situations. Not doing so risks real, near-term algorithmic harm. (shrink)

In this article I provide a critical perspective on governing the global corporation. While the papers in the 2009 special issue of Business Ethics Quarterly explore the political role of corporations I argue that they lack a sophisticated analysis of power acrossinstitutional and actor networks. The argument that corporate engagement with deliberative democracy can enhance the legitimacy of corporations does not take into account the effects of institutional, material and discursive forms of power that determine legitimacycriteria. As a result corporate (...) versions of citizenship mediate versions of social responsibility and morality, which are reflected in the institutional and political economic norms that are produced by this power/knowledge. In order to overcome the limits of corporate social responsibility there is a need to develop more democratic forms of global governance of corporations. A radical revisioning of democratic governance would also need to overcome the limits posed by sovereignty and would require new forms of multi-actor and multi-level translocal governance arrangements in an attempt to create forms of power that are more compatible with the principles of economic democracy. (shrink)

There can be good reasons to doubt the authority of a group of scientists. But those reasons do not include lack of unanimity among them. Indeed, holding science to a unanimity or near-unanimity standard has a pernicious effect on scientific deliberation, and on the transparency that is so crucial to the authority of science in a democracy. What authorizes a conclusion is the quality of the deliberation that produced it, which is enhanced by the presence of a non-dismissible minority. Scientists (...) can speak as one in more ways than one. We recommend a different sort of consensus that is partly substantive and partly procedural. It is a version of what Margaret Gilbert calls “joint acceptance” – we call it “deliberative acceptance.” It capitalizes on there being a persistent minority, and thereby encourages accurate reporting of the state of agreement and disagreement among deliberators. (shrink)

This article investigates the prospects of deliberative democracy through the analysis of small-scale deliberative events, or mini-publics, using empirical methods to understand the process of preference transformation. Evidence from two case studies suggests that deliberation corrects preexisting distortions of public will caused by either active manipulation or passive overemphasis on symbolically potent issues. Deliberation corrected these distortions by reconnecting participants’ expressed preferences to their underlying “will” as well as shaping a shared understanding of the issue.The article concludes by using these (...) insights to suggest ways that mini-public deliberation might be articulated to the broader public sphere so that the benefits might be scaled up. That mini-public deliberation does not so much change individual subjectivity as reconnect it to the expression of will suggests that scaling up the transformative effects should be possible so long as this involves communicating in the form of reasons rather than preferred outcome alone. (shrink)

Moral responsibility for outcomes in corporate settings can be ascribed either to the individual members, the corporation, or both. In the latter case, the relationship between individual and corporate responsibility has been approached as inversely proportional, such that an increase in individual responsibility leads to a corresponding decrease in corporate responsibility and vice versa. In this article, we develop a non-proportionate approach, where, under specific conditions, individual and corporate moral responsibilities interact dynamically, leading to a mutual enhancement of responsibility: the (...) more the corporation is responsible, the more the individuals become responsible and vice versa. We develop this mutually enhancing approach in terms of normative ascriptions of responsibility, while leaving aside empirical implications in terms of mutual awareness of responsibility between individuals and corporations. We explore conceptually the conditions and mechanisms that generate this mutual enhancement and also discuss the implications for research and practice. (shrink)

In our information societies, tasks and decisions are increasingly outsourced to automated systems, machines, and artificial agents that mediate human relationships, by taking decisions and acting on the basis of algorithms. This raises a critical issue: how are algorithmic procedures and applications to be appraised and governed? This question needs to be investigated, if one wishes to avoid the traps of ICTs ending up in isolating humans behind their screens and digital delegates, or harnessing them in a passive role, by (...) curtailing their freedom and autonomy. (shrink)

The article offers a critical assessment of an article on "Corporate Legitimacy as Deliberation" by Guido Palazzo and Andreas Scherer in this journal. We share the concern about the precarious legitimacy of globally active corporations, infringing on the legitimacy of democracy at large. There is no quarrel with Palazzo/Scherer's diagnosis, which focuses on the consequences of globalization and ensuing challenges for corporate social responsibilities. However, we disagree with the "solutions" offered by them. In a first step we refute the idea (...) of a legitimacy of morals, maintaining that morality is a premodern mode of creating legitimacy. Even worse, moral is becoming a dangerous commodity under conditions of fundamental global disagreements and antagonisms. We secondly refute the concept of the "politicized corporation", maintaining that Palazzo/Scherer disregard the consequences of functional differentiation of modern societies and, in particular, disregard the wisdom of political restraint and constitutional guarantees for the autonomy of different spheres of society. Finally, we refute a seemingly romantic notion of deliberation, maintaining that deliberation and deliberative democracy is a worthy idea, which, however, has no place in the real world of globalized contexts. On the other hand, we also find enough common ground and common concern with Palazzo/Scherer to validate a fruitful discourse. (shrink)

This article examines the legitimacy attached to different types of multi-stakeholder data partnerships occurring in the context of sustainable development. We develop a framework to assess the democratic legitimacy of two types of data partnerships: open data partnerships and closed data partnerships. Our framework specifies criteria for assessing the legitimacy of relevant partnerships with regard to their input legitimacy as well as their output legitimacy. We demonstrate which particular characteristics of open and closed partnerships can be expected to influence an (...) analysis of their input and output legitimacy. (shrink)

This article addresses the democratic deficit that emerges when private corporations engage in public policy, either by providing citizenship rights and global public goods (corporate citizenship) or by influencing the political system and lobbying for their economic interests (strategic corporate political activities). This democratic deficit is significant, especially when multinational corporations operate in locations where national governance mechanisms are weak or even fail, where the rule of law is absent and there is a lack of democratic control. This deficit may (...) lead to a decline in the social acceptance of the business firm and its corporate political activities and, thus, to a loss of corporate legitimacy. Under these conditions corporations may compensate for the emerging democratic deficit and reestablish their legitimacy by internalizing democratic mechanisms within their organizations, in particular in their corporate governance structures and procedures. The authors analyze the available corporate governance models with the help of a typology and discuss the possible contributions of a new form of democratic corporate governance. (shrink)

This article addresses the democratic deficit that emerges when private corporations engage in public policy, either by providing citizenship rights and global public goods (corporate citizenship) or by influencing the political system and lobbying for their economic interests (strategic corporate political activities). This democratic deficit is significant, especially when multinational corporations operate in locations where national governance mechanisms are weak or even fail, where the rule of law is absent and there is a lack of democratic control. This deficit may (...) lead to a decline in the social acceptance of the business firm and its corporate political activities and, thus, to a loss of corporate legitimacy. Under these conditions corporations may compensate for the emerging democratic deficit and reestablish their legitimacy by internalizing democratic mechanisms within their organizations, in particular in their corporate governance structures and procedures. The authors analyze the available corporate governance models with the help of a typology and discuss the possible contributions of a new form of democratic corporate governance. (shrink)

What's not to like about shared decision‐making? These programs employ specially crafted decision aids to educate patients about their treatment options and then merge the newly informed patient preferences, both general and treatment‐specific, with guidance from physicians to optimize medical decisions. Sounds great, right? Even better, recent evidence indicates that shared decision‐making programs may also help bend the proverbial cost curve by reducing the use of medical interventions that patients, now properly educated about their options, often say they do not (...) want. The notion that programs to inform and elicit patient choice might also help to align health care delivery with patient preferences for less invasive and therefore less costly treatment options seems the rarest of mutual wins in health care, in which what is best for the individual might also benefit the whole. Yet there has been scant attention to how the goals of patient care and cost‐containment, and perhaps even profitability, coincide or conflict. (shrink)