The capacity to collect and analyse data is growing exponentially. Referred to as ‘Big Data’, this scientific, social and technological trend has helped create destabilising amounts of information, which can challenge accepted social and ethical norms. Big Data remains a fuzzy idea, emerging across social, scientific, and business contexts sometimes seemingly related only by the gigantic size of the datasets being considered. As is often the case with the cutting edge of scientific and technological progress, understanding of the ethical implications (...) of Big Data lags behind. In order to bridge such a gap, this article systematically and comprehensively analyses academic literature concerning the ethical implications of Big Data, providing a watershed for future ethical investigations and regulations. Particular attention is paid to biomedical Big Data due to the inherent sensitivity of medical information. By means of a meta-analysis of the literature, a thematic narrative is provided to guide ethicists, data scientists, regulators and other stakeholders through what is already known or hypothesised about the ethical risks of this emerging and innovative phenomenon. Five key areas of concern are identified: informed consent, privacy, ownership, epistemology and objectivity, and ‘Big Data Divides’ created between those who have or lack the necessary resources to analyse increasingly large datasets. Critical gaps in the treatment of these themes are identified with suggestions for future research. Six additional areas of concern are then suggested which, although related have not yet attracted extensive debate in the existing literature. It is argued that they will require much closer scrutiny in the immediate future: the dangers of ignoring group-level ethical harms; the importance of epistemology in assessing the ethics of Big Data; the changing nature of fiduciary relationships that become increasingly data saturated; the need to distinguish between ‘academic’ and ‘commercial’ Big Data practices in terms of potential harm to data subjects; future problems with ownership of intellectual property generated from analysis of aggregated datasets; and the difficulty of providing meaningful access rights to individual data subjects that lack necessary resources. Considered together, these eleven themes provide a thorough critical framework to guide ethical assessment and governance of emerging Big Data practices. (shrink)

The literature on health and diseases is usually presented as an opposition between naturalism and normativism. This article argues that such a picture is too simplistic: there is not one opposition between naturalism and normativism, but many. I distinguish four different domains where naturalist and normativist claims can be contrasted: (1) ordinary usage, (2) conceptually clean versions of “health” and “disease,” (3) the operationalization of dysfunction, and (4) the justification for that operationalization. In the process I present new arguments in (...) response to Schwartz (2007) and Hausman (2012) and expose a link between the arguments made by Schwartz (2007) and Kingma (2010). Distinguishing naturalist claims at these four domains will allow us to make progress by (1) providing more nuanced, intermediate positions about a possible role for values in health and disease; and (2) assisting in the addressing of relativistic worries about the value-ladenness of health and disease. (shrink)

The phenomenon of distributed knowledge is well-known in epistemic logic. In this paper, a similar phenomenon in ethics, somewhat neglected so far, is investigated, namely distributed morality. The article explains the nature of distributed morality, as a feature of moral agency, and explores the implications of its occurrence in advanced information societies. In the course of the analysis, the concept of infraethics is introduced, in order to refer to the ensemble of moral enablers, which, although morally neutral per se, can (...) significantly facilitate or hinder both positive and negative moral behaviours. (shrink)

Traditionally, the manufacturer/operator of a machine is held (morally and legally) responsible for the consequences of its operation. Autonomous, learning machines, based on neural networks, genetic algorithms and agent architectures, create a new situation, where the manufacturer/operator of the machine is in principle not capable of predicting the future machine behaviour any more, and thus cannot be held morally responsible or liable for it. The society must decide between not using this kind of machine any more (which is not a (...) realistic option), or facing a responsibility gap, which cannot be bridged by traditional concepts of responsibility ascription. (shrink)

How should one react when one has a belief, but knows that other people—who have roughly the same evidence as one has, and seem roughly as likely to react to it correctly—disagree? This paper argues that the disagreement of other competent inquirers often requires one to be much less confident in one’s opinions than one would otherwise be.

This paper argues that the medical conception of health as absence of disease is a value-free theoretical notion. Its main elements are biological function and statistical normality, in contrast to various other ideas prominent in the literature on health. Apart from universal environmental injuries, diseases are internal states that depress a functional ability below species-typical levels. Health as freedom from disease is then statistical normality of function, i.e., the ability to perform all typical physiological functions with at least typical efficiency. (...) This conception of health is as value-free as statements of biological function. The view that health is essentially value-laden, held by most writers on the topic, seems to have one of two sources: an assumption that health judgments must be practical judgments about the treatment of patients, or a commitment to "positive" health beyond the absence of disease. I suggest that the assumption is mistaken, the commitment possibly misdescribed. (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)

Quassim Cassam introduces the idea of epistemic vices, character traits that get in the way of knowledge, such as closed-mindedness, intellectual arrogance, wishful thinking, and prejudice. Using examples from politics to illustrate the vices at work, he considers whether we are responsible for such failings, and what we can do about them.

Reason, we are told, is what makes us human, the source of our knowledge and wisdom. If reason is so useful, why didn't it also evolve in other animals? If reason is that reliable, why do we produce so much thoroughly reasoned nonsense? In their groundbreaking account of the evolution and workings of reason, Hugo Mercier and Dan Sperber set out to solve this double enigma. Reason, they argue with a compelling mix of real-life and experimental evidence, is not geared (...) to solitary use, to arriving at better beliefs and decisions on our own. What reason does, rather, is help us justify our beliefs and actions to others, convince them through argumentation, and evaluate the justifications and arguments that others address to us. In other words, reason helps humans better exploit their uniquely rich social environment. This interactionist interpretation explains why reason may have evolved and how it fits with other cognitive mechanisms. It makes sense of strengths and weaknesses that have long puzzled philosophers and psychologists--why reason is biased in favor of what we already believe, why it may lead to terrible ideas and yet is indispensable to spreading good ones.--. (shrink)

Self‐driving cars hold out the promise of being much safer than regular cars. Yet they cannot be 100% safe. Accordingly, we need to think about who should be held responsible when self‐driving cars crash and people are injured or killed. We also need to examine what new ethical obligations might be created for car users by the safety potential of self‐driving cars. The article first considers what lessons might be learned from the growing legal literature on responsibility for crashes with (...) self‐driving cars. Next, worries about responsibility gaps and retribution gaps from the philosophical literature are introduced. This leads to a discussion of whether self‐driving cars are a form of agents that act independently of human agents. It is suggested that it is better to analyze their apparent agency in terms of human–robot collaborations, within which humans play the most important roles. The next topic is the idea that the safety potential of self‐driving cars might create a duty to either switch to self‐driving cars or seek means of making conventional cars safer. Lastly, there is a short discussion of ethical issues related to safe human–robot coordination within mixed traffic featuring both self‐driving cars and conventional cars. (shrink)

Medical nihilism is the view that we should have little confidence in the effectiveness of medical interventions. Jacob Stegenga argues persuasively that this is how we should see modern medicine, and suggests that medical research must be modified, clinical practice should be less aggressive, and regulatory standards should be enhanced.

Although decision‐making algorithms are not new to medicine, the availability of vast stores of medical data, gains in computing power, and breakthroughs in machine learning are accelerating the pace of their development, expanding the range of questions they can address, and increasing their predictive power. In many cases, however, the most powerful machine learning techniques purchase diagnostic or predictive accuracy at the expense of our ability to access “the knowledge within the machine.” Without an explanation in terms of reasons or (...) a rationale for particular decisions in individual cases, some commentators regard ceding medical decision‐making to black box systems as contravening the profound moral responsibilities of clinicians. I argue, however, that opaque decisions are more common in medicine than critics realize. Moreover, as Aristotle noted over two millennia ago, when our knowledge of causal systems is incomplete and precarious—as it often is in medicine—the ability to explain how results are produced can be less important than the ability to produce such results and empirically verify their accuracy. (shrink)

After surveying and criticizing some earlier definitions of "disease", we propose that a general term--malady--be used to represent what all diseases, illnesses, injuries, etc., have in common. We define a malady as the suffering, or increased risk of suffering an evil in the absence of a distinct sustaining cause. We discuss the key terms in the definition: evil, distinct sustaining cause, and increased risk. We show that the role of abnormality is to clarify these terms rather than to be used (...) directly in the definition of malady. (shrink)

The paper describes a project in which the thesis of the social determinants of health is used in order to help identify groups that will be among the least advantaged members of society, when disadvantage is understood in terms of lack of genuine opportunity for secure functioning. The analysis is derived from the author's work with Avner de-Shalit in Disadvantage (Oxford University Press, 2007).

Artificial intelligence (AI) is increasingly being developed for use in medicine, including for diagnosis and in treatment decision making. The use of AI in medical treatment raises many ethical issues that are yet to be explored in depth by bioethicists. In this paper, I focus specifically on the relationship between the ethical ideal of shared decision making and AI systems that generate treatment recommendations, using the example of IBM’s Watson for Oncology. I argue that use of this type of system (...) creates both important risks and significant opportunities for promoting shared decision making. If value judgements are fixed and covert in AI systems, then we risk a shift back to more paternalistic medical care. However, if designed and used in an ethically informed way, AI could offer a potentially powerful way of supporting shared decision making. It could be used to incorporate explicit value reflection, promoting patient autonomy. In the context of medical treatment, we need value-flexible AI that can both respond to the values and treatment goals of individual patients and support clinicians to engage in shared decision making. (shrink)

Traditional conceptions of informed consent seem difficult or even impossible to apply to new technologies like biobanks, big data, or GMOs, where vast numbers of people are potentially affected, and where consequences and risks are indeterminate or even unforeseeable. Likewise, the principle has come under strain with the appropriation and monetisation of personal information on digital platforms. Over time, it has largely been reduced to bare assent to formalistic legal agreements. To address the current ineffectiveness of the norm of informed (...) consent, I suggest that we need a notion of structural injustice (on a distinctive interpretation, elaborated here, which takes account of unequal power and property relations). I then argue that in order to protect and enhance people's freedom, we have to go beyond traditional applied ethics and introduce perspectives from democratic theory and social philosophy. I attempt to show how applications of the ‘all‐affected principle’, together with new forms of democratic participation, deliberation, and representation can helpfully frame the narrower principle of informed consent. There is an important role for what we could call collective consent, and informed consent can only succeed in increasing individual agency if it is situated within enhanced forms of democratic decision‐making. (shrink)