The “la Caixa” Foundation has been experimenting with artificial intelligence (AI)-assisted decision-making geared toward alleviating the administrative burden associated with the evaluation pipeline of its flagship funding program, piloting an algorithm to detect immature project proposals before they reach the peer review stage, and suggest their removal from the selection process to a human overseer. In this article, we explore existing uses of AI by publishers and research funding organizations to automate their selection pipelines, in addition to analyzing the conditions under which the focal case corresponds to a responsible use of AI and the extent to which these conditions are met by the current implementation, highlighting challenges and areas of improvement.

From music recommendation to assessment of asylum applications, machine-learning algorithms play a fundamental role in our lives. Naturally, the rise of AI implementation strategies has brought to public attention the ethical risks involved. However, the dominant anti-discrimination discourse, too often preoccupied with identifying particular instances of harmful AIs, has yet to bring clearly into focus the more structural roots of AI-based injustice. This paper addresses the problem of AI-based injustice from a distinctively epistemic angle. More precisely, I argue that the injustice generated by the implementation of AI machines in our societies is, in some paradigmatic cases, also a form of epistemic injustice. With a particular focus on AIs employed as gatekeepers of our epistemic resources, this paper shows how their epistemically conformist behaviour is responsible for the marginalisation and the ostracism of minoritarian perspectives. Because it clarifies key structural flaws and weaknesses of current AI design, this paper helps make headway in critical discussion of current AI technologies. And because it forges new theoretical tools to understand forms of epistemic oppression, this paper also contributes to the advancement of feminist theorisation.

Depuis l'annonce du rachat de Twitter par Elon Musk, les risques quant à l'avenir du réseau social laissent entrevoir l'avènement d'un « Frankenstein 2.0 » – une notion qui fait référence aux grandes entreprises de technologie dont le contrôle échappe à leurs créateurs. Derrière cet événement se pose la question des dérives technologiques et de leurs impacts sur la société. C'est une occasion pour les gouvernements et en particulier celui du Canada, qui encourage l'ouverture des données et les politiques en faveur des technologies, de redéfinir les règles du jeu. En ce sens, quelques recommandations sont proposées en vue de consolider l'encadrement normatif des technologies au Canada. La loi sur l'intelligence artificielle et les données (LIAD), actuellement en discussion au sein du Parlement, devrait en effet aller plus loin concernant la transparence des systèmes automatisés et renforcer ses exigences en matière de gouvernance des données « algorithmiques ».

For smart cities, data-driven innovation promises societal benefits and increased well-being for residents and visitors. At the same time, the deployment of data-driven innovation poses significant ethical challenges. Although cities and other public-sector actors have increasingly adopted ethical principles, employing them in practice remains challenging. In this commentary, we use a virtue-based approach that bridges the gap between abstract principles and the daily work of practitioners who engage in and with data-driven innovation processes. Inspired by Aristotle, we describe practices of data-driven innovation in a smart city applying the concepts of virtue and phronêsis, meaning good judgment of and sensitivity to ethical issues. We use a dialogic case-study approach to study two cases of data-driven innovation in the city of Helsinki. We then describe as an illustration of how our approach can help bridge the gap between concrete practices of data-driven innovation and high-level principles. Overall, we advance a theoretically grounded, virtue-based approach, which is practice oriented and linked to the daily work of data scientists and other practitioners of data-driven innovation. Further, this approach helps understand the need for and importance of individual application of phronêsis, which is particularly important in public-sector organizations that can experience gaps between principle and practice. This importance is further intensified in cases of data-driven innovation in which, by definition, novel and unknown contexts are explored.

Position papers on artificial intelligence (AI) ethics are often framed as attempts to work out technical and regulatory strategies for attaining what is commonly called trustworthy AI. In such papers, the technical and regulatory strategies are frequently analyzed in detail, but the concept of trustworthy AI is not. As a result, it remains unclear. This paper lays out a variety of possible interpretations of the concept and concludes that none of them is appropriate. The central problem is that, by framing the ethics of AI in terms of trustworthiness, we reinforce unjustified anthropocentric assumptions that stand in the way of clear analysis. Furthermore, even if we insist on a purely epistemic interpretation of the concept, according to which trustworthiness just means measurable reliability, it turns out that the analysis will, nevertheless, suffer from a subtle form of anthropocentrism. The paper goes on to develop the concept of strange error, which serves both to sharpen the initial diagnosis of the inadequacy of trustworthy AI and to articulate the novel epistemological situation created by the use of AI. The paper concludes with a discussion of how strange error puts pressure on standard practices of assessing moral culpability, particularly in the context of medicine.

Taking a law-and-governance approach, this article addresses legal certainty in international human rights law as it applies to artificial intelligence (AI). After introducing key issues concerning legal certainty, a comparative analysis of AI law-and-governance initiatives at the international, regional and national levels is undertaken. The article argues that many initiatives contribute to increased legal certainty and can partially compensate for some of the shortcomings of the international human rights law framework, but that further clarification is badly needed. This is especially true for the responsibilities of private businesses which are developing AI and the corpus of human rights beyond privacy and data protection.

Responsible innovation in artificial intelligence (AI) calls for public deliberation: well-informed “deep democratic” debate that involves actors from the public, private, and civil society sectors in joint efforts to critically address the goals and means of AI. Adopting such an approach constitutes a challenge, however, due to the opacity of AI and strong knowledge boundaries between experts and citizens. This undermines trust in AI and undercuts key conditions for deliberation. We approach this challenge as a problem of situating the knowledge of actors from the AI industry within a deliberative system. We develop a new framework of responsibilities for AI innovation as well as a deliberative governance approach for enacting these responsibilities. In elucidating this approach, we show how actors from the AI industry can most effectively engage with experts and nonexperts in different social venues to facilitate well-informed judgments on opaque AI systems and thus effectuate their democratic governance.

Our ability to locate moral responsibility is often thought to be a necessary condition for conducting morally permissible medical practice, engaging in a just war, and other high-stakes endeavors. Yet, with increasing reliance upon artificially intelligent systems, we may be facing a widening responsibility gap, which, some argue, cannot be bridged by traditional concepts of responsibility. How then, if at all, can we make use of crucial emerging technologies? According to Colin Allen and Wendell Wallach, the advent of so-called ‘artificial moral agents’ (AMAs) is inevitable. Still, this notion may seem to push back the problem, leaving those who have an interest in developing autonomous technology with a dilemma. We may need to scale-back our efforts at deploying AMAs (or at least maintain human oversight); otherwise, we must rapidly and drastically update our moral and legal norms in a way that ensures responsibility for potentially avoidable harms. This paper invokes contemporary accounts of responsibility in order to show how artificially intelligent systems might be held responsible. Although many theorists are concerned enough to develop artificial conceptions of agency or to exploit our present inability to regulate valuable innovations, the proposal here highlights the importance of—and outlines a plausible foundation for—a workable notion of artificial moral responsibility.

Artificial intelligence (AI)-supported systems have transformative applications in the humanitarian sector but they also pose unique risks for human rights, even when used with the best intentions. Drawing from research and expert consultations conducted across the globe in recent years, this paper identifies key points of consensus on how humanitarian practitioners can ensure that AI augments – rather than undermines – human interests while being rights-respecting. Specifically, these consultations emphasized the necessity of an anchoring framework based on international human rights law as an essential baseline for ensuring that human interests are embedded in AI systems. Ethics, in addition, can play a complementary role in filling gaps and elevating standards above the minimum requirements of international human rights law. This paper summarizes the advantages of this framework, while also identifying specific tools and best practices that either already exist and can be adapted to the AI context, or that need to be created, in order to operationalize this human rights framework. As the COVID crisis has laid bare, AI will increasingly shape the global response to the world's toughest problems, especially in the development and humanitarian sector. To ensure that AI tools enable human progress and contribute to achieving the Sustainable Development Goals, humanitarian actors need to be proactive and inclusive in developing tools, policies and accountability mechanisms that protect human rights.

Artificial intelligence has dramatically changed the world as we know it, but is yet to fully embrace ‘hot’ cognition, i.e., the way an intelligent being's thinking is affected by their emotional state. Artificial intelligence encompassing hot cognition will not only usher in enhanced machine-human interactions, but will also promote a much needed ethical approach. Theory of Mind, the ability of the human mind to attribute mental states to others, is a key component of hot cognition. To endow machines with (limited) Theory of Mind capabilities, computer scientists will need to work closely with psychiatrists, psychologists and neuroscientists. They will need to develop new models, but also to formally define what problems need to be solved and how the results should be assessed.