Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are 'black boxes'. The initial response in the literature was a demand for 'explainable AI'. However, recently, several authors have suggested that making AI more explainable or 'interpretable' is likely to be at the cost of the accuracy of these systems and that prioritising interpretability in medical AI may constitute a 'lethal prejudice'. In this paper, we defend the value of interpretability (...) in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realised. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems , may itself constitute a form of lethal prejudice that may diminish the benefits of AI to - and perhaps even harm - patients. (shrink)

Technology corporations and the emerging digital health market are exerting increasing influence over the public healthcare agendas forming around the application of mobile medical devices. By promising quick and cost-effective technological solutions to complex healthcare problems, they are attracting the interest of funders, researchers, and policymakers. They are also shaping the public facing discourse, advancing an overwhelmingly positive narrative predicting the benefits of wearable medical devices to include personalised medicine, improved efficiency and quality of care, the empowering of under-resourced communities, (...) and delivery of health services previously unavailable to the citizens of developing countries. Typically techno-optimist in their description, the key barriers to this impending inflection point in healthcare are identified as technical issues such as short battery life and a lack of data protection. However, this tech innovation narrative is consistent with problematic ethical, social, and political assumptions that have practical and normative effects, and risk, one, undermining the real clinical potential of wearable devices and, two, designing social inequality and injustice into our mobile health interventions in global healthcare. I argue that the foundational assumptions dealing with the just distribution of healthcare ‘goods’, the individual as part of society, and the political framing of future healthcare policy devalue equity and despite what they promise cannot meet the distinctive needs of individuals and groups that do not conform to a standardised concept of care-receiver. (shrink)

Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are “black boxes.” The initial response in the literature was a demand for “explainable AI.” However, recently, several authors have suggested that making AI more explainable or “interpretable” is likely to be at the cost of the accuracy of these systems and that prioritizing interpretability in medical AI may constitute a “lethal prejudice.” In this paper, we defend the value of interpretability (...) in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realized. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems, may itself constitute a form of lethal prejudice that may diminish the benefits of AI to—and perhaps even harm—patients. (shrink)

It is likely that sex robots will exist in the near future, making the effect they might have on human relationships a pressing concern. In this future world, we can imagine sex robots shaping our personal and social relationships through their unique access to, and potential for influencing, our most intimate of behaviours. We investigate whether they might be employed to influence social behaviours in a positive way. The paper begins with an account of the state of the art, acknowledges (...) powerful feminist criticisms that have been made of sex robots, and evaluates suggestions that it might be possible to design sex robots which do not raise these concerns and which might even work to influence social behaviours in a positive way. It then outlines a number of ways that sex robots might be used to educate, “nudge”, and influence people in positive ways. It defends the idea that it would be ethical to use sex robots to promote socially positive behaviours — behaviours that benefit others and improve social cohesion, such as fostering respect and empathy for persons — but not to promote commercial products for parochial interests. We argue that the former project could advance individual and social welfare, while preserving personal autonomy — a minimum requirement of which is the ability to make informed decisions — whereas the latter depends on a lack of transparency and democratic control for its success, targets the vulnerability of the user to achieve its ends, and reinforces the problematic symbolism of negatively gendered sexuality. If sex robot design and application meet public requirements of transparency — enabling informed consent and reflective decision making — and democratic oversight — promoting accountability and the sharing of power with the public — it is conceivable that sex robots might assist, rather than harm society. (shrink)

The World Health Organisation (WHO) is leading a global effort to deliver improved diagnostic testing to people living in low‐resource settings. A reliance on the healthcare technologies marketplace and industry, shapes many aspects of the WHO project, and in this situation normative guidance comes by way of the ASSURED criteria — Affordable, Sensitive, Specific, User‐friendly, Rapid and robust, Equipment‐free, and Delivered. While generally improving access to diagnostics, I argue that the ASSURED approach to distributive justice — efficiency — and assessment (...) of worth — productivity — may constrain efforts to deliver timely and accurate diagnosis in the developing world equitably by holding back new and innovative diagnostics and indirectly encouraging program and device design that may unfairly discriminate against certain groups. Even as we try to overcome the problem of global healthcare injustice, we may be entrenching disadvantage. I present my critique of ASSURED by 1) referencing Boltanski and Thévenot's theory of orders of worth to highlight the industrial and market foundations of the ASSURED guidelines; 2) comparing ASSURED with other normative guides that elevate the importance of civic responsibility in evaluations of distributive justice; 3) presenting a case study of the failed promise of microfluidic diagnostic devices. I conclude that a new approach to normative guidance is required to assess the value of developing world diagnostics, preferably, one that does not force global public goods into the marketplace. (shrink)

We prove a theorem (1.7) about partial orders which can be viewed as a version of the Barwise compactness theorem which does not mention logic. The Barwise compactness theorem is easily equivalent to 1.7 + "Every Henkin set has a model". We then make the observation that 1.7 gives us the definability of forcing for quantifier-free sentences in the forcing language and use this to give a direct proof of the truth and definability lemmas of forcing.

Hastings Center Report, Volume 52, Issue 2, Page 41-48, March‐April 2022.

Angelaki, Volume 16, Issue 1, Page 1-3, 01Mar2011.