Nowadays, new directions for the development of artificial intelligence (AI) have emerged, the task has been set to develop artificial general intelligence (AGI), which is able to go beyond the narrow AI, gain a high degree of autonomy, independently solve problems in different environmental conditions and thus have the ability to perform the functions of natural intelligence. In this regard, important philosophical, theoretical, and methodological questions arise concerning the definition and evaluation of the social significance of new AI achievements, especially (...) regarding the correlation of their socio-humanitarian and technological aspects. It is necessary to overcome a purely technocratic approach, which usually ignores the negative consequences of digitalization, the possible risks and threats of AI development. These difficulties are conditioned by the paradigm gap between the system of concepts used to describe technologies and the systems of concepts specific to socio-humanitarian descriptions and explanations. The so-called ontological paradox arises during digital transformation and the introduction of AI into social systems. An ontological paradox is also an epistemological paradox, since any statement of an ontological type presupposes its epistemological justification. To overcome this gap, V.E. Lepskiy proposes the conception of self-developing polysubjective environments – cybernetics of the third order. This conception allows to create a conceptual “bridge” between two systems of definitions that do not have direct logical connections between them. A productive tool for this can be the information approach, which is widely used to solve such interdisciplinary problems. This makes possible to theoretically correctly connect technological and socio-humanitarian descriptions in a single conceptual structure and to analyze main socio-humanitarian criteria for evaluating digital innovations. The article discusses in detail the main provisions of the information approach and its application for the development of AGI systems, the socio-humanitarian assessment of AI autonomous development, and the resolution of security issues. (shrink)

A self-fulfilling prophecy in neuroprognostication occurs when a patient in coma is predicted to have a poor outcome, and life-sustaining treatment is withdrawn on the basis of that prediction, thus directly bringing about a poor outcome for that patient. In contrast to the predominant emphasis in the bioethics literature, we look beyond the moral issues raised by the possibility that an erroneous prediction might lead to the death of a patient who otherwise would have lived. Instead, we focus on the (...) problematic epistemic consequences of neuroprognostic SFPs in settings where research and practice intersect. When this sort of SFP occurs, the problem is that physicians and researchers are never in a position to notice whether their original prognosis was correct or incorrect, since the patient dies anyway. Thus, SFPs keep us from discerning false positives from true positives, inhibiting proper assessment of novel prognostic tests. This epistemic problem of SFPs thus impedes learning, but ethical obligations of patient care make it difficult to avoid SFPs. We then show how the impediment to catching false positive indicators of poor outcome distorts research on novel techniques for neuroprognostication, allowing biases to persist in prognostic tests. We finally highlight a particular risk that a precautionary bias towards early withdrawal of life-sustaining treatment may be amplified. We conclude with guidelines about how researchers can mitigate the epistemic problems of SFPs, to achieve more responsible innovation of neuroprognostication for patients in coma. (shrink)

There are growing discontinuities between the research practices of data science and established tools of research ethics regulation. Some of the core commitments of existing research ethics regulations, such as the distinction between research and practice, cannot be cleanly exported from biomedical research to data science research. Such discontinuities have led some data science practitioners and researchers to move toward rejecting ethics regulations outright. These shifts occur at the same time as a proposal for major revisions to the Common Rule—the (...) primary regulation governing human-subjects research in the USA—is under consideration for the first time in decades. We contextualize these revisions in long-running complaints about regulation of social science research and argue data science should be understood as continuous with social sciences in this regard. The proposed regulations are more flexible and scalable to the methods of non-biomedical research, yet problematically largely exclude data science methods from human-subjects regulation, particularly uses of public datasets. The ethical frameworks for Big Data research are highly contested and in flux, and the potential harms of data science research are unpredictable. We examine several contentious cases of research harms in data science, including the 2014 Facebook emotional contagion study and the 2016 use of geographical data techniques to identify the pseudonymous artist Banksy. To address disputes about application of human-subjects research ethics in data science, critical data studies should offer a historically nuanced theory of “data subjectivity” responsive to the epistemic methods, harms and benefits of data science and commerce. (shrink)

This article reflects on the relevance and applicability of the Belmont Report nearly four decades after its original publication. In an exploration of criticisms that have been raised in response to the report and of significant changes that have occurred within the context of biomedical research, five primary themes arise. These themes include the increasingly vague boundary between research and practice, unique harms to communities that are not addressed by the principle of respect for persons, and how growing complexity and (...) commodification in research have shed light on the importance of transparency. The repercussions of Belmont's emphasis on the protection of vulnerable populations is also explored, as is the relationship between the report's ethical principles and their applications. It is concluded that while the Belmont Report was an impressive response to the ethical issues of its day, the field of research ethics involving human subjects may have outgrown it. (shrink)

In revising the Federal Policy for the Protection of Human Subjects between 2009 and 2018, regulators devoted the vast bulk of their attention to debates over biomedical research. They lacked both expertise in and concern about the social sciences and humanities, yet they imposed their will on experts in those fields. The revision process was secretive, spasmodic, and unrepresentative, especially compared to rulemaking in Canada, where social scientists participate in the process, and revisions take place every few years. The result (...) was a final rule that offers some wins for social science and the humanities, but that fails to solve the problems identified by Ezekiel Emanuel and in the 2011 advance notice of proposed rulemaking. (shrink)

In this article we attempt to answer the question of how the ethical and conceptual framework (ECF) for a learning health‐care system (LHS) affects some of the main controversies in research ethics by addressing five key problems of research ethics: (a) What is the difference between practice and research? (b) What is the relationship between research ethics and clinical ethics? (c) What is the ethical relevance of the principle of clinical equipoise? (d) Does participation in research require a higher standard (...) of informed consent than the practice of medicine? and (e) What ethical principle should take precedence in medicine? These questions allow us to construct two opposite idealized positions on the distinction between research and practice: the integration model and the segregation model of research and practice. We then compare the ECF for an LHS with these two idealized positions. We argue that the ECF for a LHS does not, in fact, solve these problems, but that it is a third, separate position in the relationship between research ethics and clinical ethics. Moreover, we suggest that the ECF for a LHS raises new ethical problems that require additional ethical analysis and justification. Our article contributes to the discussion on the relationship between research ethics and clinical ethics, revealing that although a learning health‐care system may significantly change the landscape of health care, some ethical dilemmas still require resolving on both theoretical and policy‐making levels. (shrink)

Calls are increasing for American health care to be organized as a learning health care system, defined by the Institute of Medicine as a health care system “in which knowledge generation is so embedded into the core of the practice of medicine that it is a natural outgrowth and product of the healthcare delivery process and leads to continual improvement in care.” We applaud this conception, and in this paper, we put forward a new ethics framework for it. No such (...) framework has previously been articulated. The goals of our framework are twofold: to support the transformation to a learning health care system and to help ensure that learning activities carried out within such a system are conducted in an ethically acceptable fashion. (shrink)

Using mobile health research as an extended example, this article provides an overview of when the Common Rule “applies” to a variety of activities, what might be meant when one says that the Common Rule does or does not “apply,” the extent to which these different meanings of “apply” matter, and, when the Common Rule does apply, how it applies.

Debates about how knowledge is made and valued in evidence-based medicine (EBM) have yet to understand what discursive, social, and historical conditions allowed the EBM approach to stabilize and proliferate across western medical education. This paper uses a genealogical approach to examine the epistemological tensions that emerged as a result of various problematizations of uncertainty in medical practice. I explain how the problematization of uncertainty in the literature and the contingency of specific social, political, economic, and historical relations allowed the (...) EBM approach to become a programmatic and pedagogical focus of the Faculty of Medicine at McMaster University and beyond. (shrink)

The Belmont Report’s distinction between research and the practice of accepted therapy has led various authors to suggest that these purportedly distinct activities should be governed by different ethical principles. We consider some of the ethical consequences of attempts to separate the two and conclude that separation fails along ontological, ethical, and epistemological dimensions. Clinical practice and clinical research, as with yin and yang, can be thought of as complementary forces interacting to form a dynamic system in which the whole (...) exceeds the sum of its parts. Just as effective clinical practice cannot exist without clinical research, meaningful clinical research requires the context of clinical practice. We defend this thesis by triangulation, that is, by outlining how multiple investigators have reached this conclusion on the basis of varied theoretical and applied approaches. More confidence can be placed in a result if different methods/viewpoints have led to that result. (shrink)

Arguments in favor of greater research-practice integration in medicine have tended to be ethical, political, or pragmatic. There are good epistemic reasons to pursue greater integration, and it is important to think through these reasons in order to avoid inadvertently designing new systems in ways that replicate the epistemic elitism common within current systems. Meaningful transformation within health care is possible with close attention to all reasons in favor of greater research-practice integration, including epistemic reasons.

Medical practice is ideally based on robust, relevant research. However, the lack of disease-modifying treatments for Alzheimer’s disease has motivated “innovative practice” to improve patients’ well-being despite insufficient evidence for the regular use of such interventions in health systems treating millions of patients. Innovative or new non-validated practice poses at least three distinct ethical questions: first, about the responsible application of new non-validated practice to individual patients ; second, about the way in which data from new non-validated practice are communicated (...) via the scientific and lay press ; and third, about the prospect of making new non-validated interventions widely available before more definitive testing. We argue that the authors of metabolic enhancement protocols for Alzheimer’s disease have overstated the evidence in favor of these interventions within the scientific and lay press, failing to communicate weaknesses in their data and uncertainty about their conclusions. Such unmeasured language may create false hope, cause financial harm, undermine informed consent, and frustrate the production of generalizable knowledge necessary to face the societal problems posed by this devastating disease. We therefore offer more stringent guidelines for responsible innovation in the treatment of Alzheimer’s disease. (shrink)

Human genomics is a translational field spanning research, clinical care, public health, and direct-to-consumer testing. However, law differs across these domains on issues including liability, consent, promoting quality of analysis and interpretation, and safeguarding privacy. Genomic activities crossing domains can thus encounter confusion and conflicts among these approaches. This paper suggests how to resolve these conflicts while protecting the rights and interests of individuals sequenced. Translational genomics requires this more translational approach to law.

Artificial Intelligence (AI) in the 21st century is no longer perceived as a purely technological phenomenon, more and more becoming a social and humanitarian phenomenon that develops in a complex context of cultural, value, philosophical, and ethical aspects of human life. The impact of AIrelated technologies on contemporary society is still difficult to assess fully, which does not prevent enthusiastic researchers and political leaders from attempting to define a value framework that will ensure the use of AI for societal development. (...) As interest in AI grows, more and more technologically advanced countries in the world are creating their own strategies for the development and use of this technological marvel of the 21st century. These pioneering documents often seem vague and indefinite, but nevertheless they allow us to assess how the political and scientific and technological elites of these countries see the value orientations of AI technology development, both nationally and internationally. The article presents a philosophical analysis of the value orientations of AI technology development in the USA and China – modern scientific and technological leaders in this field – on the basis of strategic documents defining the development and application of AI in these countries from the position of post-non-classical scientific rationality. The authors of the article conclude that, at the contemporary post-non-classical stage of science development, the value component is not only one of the integral components of scientific and technological activities but may be decisive in determining the goals and objectives of high-tech development at the state level. (shrink)