Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, (...) and procedures cannot be meaningfully understood by human practitioners. When AI systems reach this level of complexity, we can also speak of black-box medicine. In this paper, we want to argue that black-box medicine conflicts with core ideals of patient-centered medicine. In particular, we claim, black-box medicine is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient. (shrink)

This book discusses problems of conceptual analysis as well as normative issues of vital contemporary concern.

This paper focuses on the fact that AI is predominantly portrayed as white—in colour, ethnicity, or both. We first illustrate the prevalent Whiteness of real and imagined intelligent machines in four categories: humanoid robots, chatbots and virtual assistants, stock images of AI, and portrayals of AI in film and television. We then offer three interpretations of the Whiteness of AI, drawing on critical race theory, particularly the idea of the White racial frame. First, we examine the extent to which this (...) Whiteness might simply reflect the predominantly White milieus from which these artefacts arise. Second, we argue that to imagine machines that are intelligent, professional, or powerful is to imagine White machines because the White racial frame ascribes these attributes predominantly to White people. Third, we argue that AI racialised as White allows for a full erasure of people of colour from the White utopian imaginary. Finally, we examine potential consequences of the racialisation of AI, arguing it could exacerbate bias and misdirect concern. (shrink)

The ever-increasing application of algorithms to decision-making in a range of social contexts has prompted demands for algorithmic accountability. Accountable decision-makers must provide their decision-subjects with justifications for their automated system’s outputs, but what kinds of broader principles should we expect such justifications to appeal to? Drawing from political philosophy, I present an account of algorithmic accountability in terms of the democratic ideal of ‘public reason’. I argue that situating demands for algorithmic accountability within this justificatory framework enables us to (...) better articulate their purpose and assess the adequacy of efforts toward them. (shrink)

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves, the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected since sophisticated algorithms usually are (...) inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante. (shrink)

All humans have an equal basic moral status. They possess the same fundamental rights, and the comparable interests of each person should count the same in calculations that determine social policy. Neither supposed racial differences, nor skin color, sex, sexual orientation, ethnicity, intelligence, nor any other differences among humans negate their fundamental equal worth and dignity. These platitudes are virtually universally affirmed. A white supremacist racist or an admirer of Adolf Hitler who denies them is rightly regarded as beyond the (...) pale of civilized dialogue. However, a very simple line of argument developed by Peter Singer challenges our understanding of these platitudes and forces us to rethink the basis and nature of the moral equality of all humans. What I call the “Singer Problem” arises if one accepts that the morally significant cognitive capacities that are relevant to the determination of the fundamental moral status of a being vary from individual to individual by degree. The problem is to specify a moral principle determining fundamental moral status that assigns a superior status to humans compared to other animals on the basis of the superior cognitive capacities of humans but also assigns all human persons an equal fundamental moral status regardless of their differing cognitive capacities. More broadly posed, the problem is to specify moral principles that yield intuitively satisfactory implications for the treatment of human individuals and other individual animals given that cognitive capacities differ across species and individuals. A search for a resolution of the Singer problem leads to disappointing results. (shrink)

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves, the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected since sophisticated algorithms usually are (...) inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante. (shrink)

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves (“gaming the system” in particular), the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected (...) since sophisticated algorithms usually are inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante. (shrink)

This paper considers possible future legislation that requires the exclusive use of autonomous cars. The author develops and defends a ‘Liberal Argument Against Mandated Autonomous Cars’, which argues that such a law would be incompatible with classical liberalism, provided that the following condition holds: In the event where the car must ‘choose’ between running over a young person or an old person, or both, autonomous cars are programmed to respond by running over old people in order to save young people. (...) Such a law requiring the use of these cars, provided that these assumptions hold, would violate the important value in classical liberalism that all individuals ought to be treated equally before the law. The paper concludes by arguing that alternative ways of dealing with this problem come with their own set of unpalatable problems. (shrink)

People recognize faces of their own race more accurately than faces of other races. The “contact” hypothesis suggests that this “other‐race effect” occurs as a result of the greater experience we have with own‐ versus other‐race faces. The computational mechanisms that may underlie different versions of the contact hypothesis were explored in this study. We replicated the other‐race effect with human participants and evaluated four classes of computational face recognition algorithms for the presence of an other‐race effect. Consistent with the (...) predictions of a developmental contact hypothesis, “experience‐based models” demonstrated an other‐race effect only when the representational system was developed through experience that warped the perceptual space in a way that was sensitive to the overall structure of the model's experience with faces of different races. When the model's representation relied on a feature set optimized to encode the information in the learned faces, experience‐based algorithms recognized minority‐race faces more accurately than majority‐race faces. The results suggest a developmental learning process that warps the perceptual space to enhance the encoding of distinctions relevant for own‐race faces. This feature space limits the quality of face representations for other‐race faces. (shrink)

This essay examines ethical aspects of the use of facial recognition technology for surveillance purposes in public and semipublic areas, focusing particularly on the balance between security and privacy and civil liberties. As a case study, the FaceIt facial recognition engine of Identix Corporation will be analyzed, as well as its use in “Smart” video surveillance systems in city centers and airports. The ethical analysis will be based on a careful analysis of current facial recognition technology, of its use in (...) Smart CCTV systems, and of the arguments used by proponents and opponents of such systems. It will be argued that Smart CCTV, which integrates video surveillance technology and biometric technology, faces ethical problems of error, function creep and privacy. In a concluding section on policy, it will be discussed whether such problems outweigh the security value of Smart CCTV in public places. (shrink)

In this paper I present the position that the use of face recognition technology (FRT) in law enforcement and in business is restrictive of individual autonomy. I reason that FRT severely undermines autonomous self-determination by hobbling the idea of freedom of the will. I distinguish this position from two other common arguments against surveillance technologies: the privacy argument (that FRT is an invasion of privacy) and the objective freedom argument (that FRT is restrictive of one's freedom to act). To make (...) this case, I suggest that autonomy itself is predicated on the possibility of acting ethically, of freely willing moral laws. I then claim that autonomous self-determination is established as self-determination via social interactions with others. If we conceptualize self-determination as a relation of establishing a claim to individual autonomy in a community of others, we can see how planned uses of FRT subvert possibilities for the establishment of socially recognized agency. FRT not only confuses the process of asking ethical questions but it also imposes the immanent likelihood that all actions are taken not by self-directed, free agents, but by passive subjects in the interest of abiding by the institutionally enforced law. (shrink)