The difficulty of explaining the outputs of artificial intelligence (AI) models and what has led to them is a notorious ethical problem wherever these technologies are applied, including in the medical domain, and one that has no obvious solution. This paper examines the proposal, made by Luciano Floridi and colleagues, to include a new ‘principle of explicability’ alongside the traditional four principles of bioethics that make up the theory of ‘principlism’. It specifically responds to a recent set of criticisms that (...) challenge the supposed need for such a principle to perform an enabling role in relation to the traditional four principles and therefore suggest that these four are sufficient without the addition of explicability. The paper challenges the critics’ premise that explicability cannot be an ethical principle like the classic four because it is explicitly subordinate to them. It argues instead that principlism in its original formulation locates the justification for ethical principles in a midlevel position such that they mediate between the most general moral norms and the contextual requirements of medicine. This conception of an ethical principle then provides a mold for an approach to explicability on which it functions as an enabling principle that unifies technical/epistemic demands on AI and the requirements of high-level ethical theories. The paper finishes by anticipating an objection that decision-making by clinicians and AI fall equally, but implausibly, under the principle of explicability’s scope, which it rejects on the grounds that human decisions, unlike AI’s, can be explained by their social environments. (shrink)

The use of AI algorithms in criminal trials has been the subject of very lively ethical and legal debates recently. While there are concerns over the lack of accuracy and the harmful biases that certain algorithms display, new algorithms seem more promising and might lead to more accurate legal decisions. Algorithms seem especially relevant for bail decisions, because such decisions involve statistical data to which human reasoners struggle to give adequate weight. While getting the right legal outcome is a strong (...) desideratum of criminal trials, advocates of the relational theory of procedural justice give us good reason to think that fairness and perceived fairness of legal procedures have a value that is independent from the outcome. According to this literature, one key aspect of fairness is trustworthiness. In this paper, I argue that using certain algorithms to assist bail decisions could increase three diferent aspects of judges’ trustworthiness: (1) actual trustworthiness, (2) rich trustworthiness, and (3) perceived trustworthiness. (shrink)

The contention that medical artificial intelligence (AI) should be ‘explainable’ is widespread in contemporary philosophy and in legal and best practice documents. Yet critics argue that ‘explainability’ is not a stable concept; non-explainable AI is often more accurate; mechanisms intended to improve explainability do not improve understanding and introduce new epistemic concerns; and explainability requirements are ad hoc where human medical decision-making is often opaque. A recent ‘political response’ to these issues contends that AI used in high-stakes scenarios, including medical (...) AI, must be explainable to meet basic standards of legitimacy: People are owed reasons for decisions that impact their vital interests, and this requires explainable AI. This article demonstrates why the political response fails. Attending to systemic considerations, as its proponents desire, suggests that the political response is subject to the same criticisms as other arguments for explainable AI and presents new issues. It also suggests that decision-making about non-explainable medical AI can meet public reason standards. The most plausible version of the response amounts to a simple claim that public reason demands reasons why AI is permitted. But that does not actually support explainable AI or respond to criticisms of strong requirements for explainable medical AI. (shrink)

L’une des fonctions des institutions publiques des démocraties libérales est de formuler des recommandations à l’attention des décideurs. Or, les institutions publiques savent que leurs recommandations seront souvent ignorées en partie par le décideur. Cette situation de « conformité partielle » aux recommandations soulève plusieurs problèmes de nature philosophique pour les institutions. En nous appuyant sur une analyse de 570 recommandations tirées de 40 documents et rapports du secteur public québécois, nous identifions deux enjeux entourant la structure des recommandations issues (...) du secteur public. (shrink)

As opaque algorithmic systems take up a larger and larger role in shaping our lives, calls for explainability in various algorithmic systems have increased. Many moral and political philosophers have sought to vindicate these calls for explainability by developing theories on which decision-subjects—that is, individuals affected by decisions—have a moral right to the explanation of the systems that affect them. Existing theories tend to suggest that the right to explanation arises solely in virtue of facts about how decision-subjects are affected (...) by opaque systems. But this ignores an important part of the normative landscape: sometimes, facts about a decision-maker’s rights and freedoms can make a difference to whether she owes anyone explanations of her decisions. I illustrate the point by calling attention to examples of artistic, romantic, and supererogatory altruistic decisions; in at least some cases of this sort, even highly influential decisions can legitimately be kept entirely private and opaque. (shrink)

Meaningful human control (MHC) is increasingly becoming an important topic in AI ethics beyond the domain of autonomous weapons systems. MHC has been conceptualized, analyzed, and applied. However, in this article, I show how all the current attempts at realizing MHC have fallen short because we have not taken the important first step of deciding what machines should and should not be doing in the first place. We must first ensure that the output we have delegated to the machine is (...) appropriate – only then do we have to do the work required to ensure that MHC is realized. Here, I argue that machines should not be evaluating – that is, we should not be delegating evaluative outputs to machines. This is practically important because machines that have evaluative outputs cannot be evaluated for efficacy. We can’t say how effective they are. This is ethically important because machines don’t have the moral agency required to make evaluations. Furthermore, machines should not be in a position to change our values – which they would be if they were evaluating. Finally, evaluations are judged based on the considerations used to reach a particular judgment. Contemporary AI cannot provide these justifying considerations – so we have no way of evaluating their evaluative outputs. (shrink)

One of the key responsibilities of public institutions in liberal democracies is to formulate recommendations for decision makers. However, public institutions realize that decision makers will often partly ignore their recommendations. This situation of “partial compliance” with recommendations raises a number of philosophical issues for institutions. Based on an analysis of 570 recommendations drawn from 40 Quebec public-sector documents and reports, we identify two issues surrounding the structure of public-policy recommendations.

Artificially intelligent machines are different in kind from all previous machines and tools. While many are used for relatively benign purposes, the types of artificially intelligent machines that we should care about, the ones that are worth focusing on, are the machines that purport to replace humans entirely and thereby engage in what Brian Cantwell Smith calls “judgment.” As impressive as artificially intelligent machines are, their abilities are still derived from humans and as such lack the sort of normative commitments (...) that humans have. So while artificially intelligent machines possess a great capacity for “reckoning,” to use Smith’s terminology, i.e., a calculative prowess of extraordinary utility and importance, they still lack the kind of considered human judgment that accompanies the ethical commitment and responsible action we humans must ultimately aspire toward. But there is a perfect technological storm brewing. Artificially intelligent machines are analogous to a perfect storm in that such machines involve the convergence of a number of factors that threaten our ability to behave ethically and maintain meaningful human control over the outcomes of processes involving artificial intelligence. I argue that the storm in the context of artificially intelligent machines makes us vulnerable to moral complacency. That is, this perfect technological storm is capable of lulling people into a state in which they abdicate responsibility for decision-making and behaviour precipitated by the use of artificially intelligent machines, a state that I am calling “moral complacency.” I focus on three salient problems that converge to make us especially vulnerable to becoming morally complacent and losing meaningful human control. The first problem is that of transparency/opacity. The second problem is that of overtrust in machines, often referred to as the automation bias. The third problem is that of ascribing responsibility. I examine each of these problems and how together they threaten to render us morally complacent. (shrink)

Since the early days of artificial intelligence (AI), many logics have been explored as tools for knowledge representation and reasoning. In the spirit of the Crossley Festscrift and recognizing John Crossley’s diverse interests and his legacy in both mathematical logic and computer science, I discuss examples from my own research that sit in the overlap of logic and AI, with a focus on supporting human–AI interactions.

Istoria paralelă a evoluției inteligenței umane și a inteligenței artificiale este o călătorie fascinantă, evidențiind căile distincte, dar interconectate, ale evoluției biologice și inovației tehnologice. Această istorie poate fi văzută ca o serie de evoluții interconectate, fiecare progres în inteligența umană deschizând calea pentru următorul salt în inteligența artificială. Inteligența umană și inteligența artificială s-au împletit de mult timp, evoluând în traiectorii paralele de-a lungul istoriei. Pe măsură ce oamenii au căutat să înțeleagă și să reproducă inteligența, IA a apărut (...) ca un domeniu dedicat creării de sisteme capabile de sarcini care necesită în mod tradițional intelect uman. Această carte analizează rădăcinile evolutive ale inteligenței, explorează apariția inteligenței artificiale, analizează istoria paralelă a inteligenței umane și a inteligenței artificiale, urmărind dezvoltarea lor, interacțiunile și impactul profund pe care l-au avut una asupra celeilalte, și imaginează peisajele viitoare în care converg inteligența umană și cea artificială. Să explorăm această istorie, comparând reperele cheie și evoluțiile din ambele tărâmuri. (shrink)

The parallel history of the evolution of human intelligence and artificial intelligence is a fascinating journey, highlighting the distinct but interconnected paths of biological evolution and technological innovation. This history can be seen as a series of interconnected developments, each advance in human intelligence paving the way for the next leap in artificial intelligence. Human intelligence and artificial intelligence have long been intertwined, evolving in parallel trajectories throughout history. As humans have sought to understand and reproduce intelligence, AI has emerged (...) as a field dedicated to creating systems capable of tasks that traditionally require human intellect. This book examines the evolutionary roots of intelligence, explores the emergence of artificial intelligence, examines the parallel history of human intelligence and artificial intelligence, tracing their development, interactions, and profound impact they have had on each other, and envisions future landscapes where intelligence converges human and artificial. Let's explore this history, comparing key milestones and developments in both realms. (shrink)