Large language models increasingly support multiple languages, yet most benchmarks for gender bias remain English-centric.We introduce EuroGEST, a dataset designed to measure gender-stereotypical reasoning in LLMs across English and 29 European languages.Eu-roGEST builds on an existing expert-informed benchmark covering 16 gender stereotypes, expanded in this work using translation tools, quality estimation metrics, and morphological heuristics.Human evaluations confirm that our data generation method results in high accuracy of both translations and gender labels across languages.We use EuroGEST to evaluate 24 multilingual language models from six model families, demonstrating that the strongest stereotypes in all models across all languages are that women are beautiful, empathetic and neat and men are leaders, strong, tough and professional.We also show that larger models encode gendered stereotypes more strongly and that instruction finetuned models continue to exhibit gendered stereotypes.Our work highlights the need for more multilingual studies of fairness in LLMs and offers scalable methods and resources to audit gender bias across languages.

The development of increasingly agentic and human-like AI assistants, capable of performing a wide range of tasks on user's behalf over time, has sparked heightened interest in the nature and bounds of human interactions with AI. Such systems may indeed ground a transition from task-oriented interactions with AI, at discrete time intervals, to ongoing relationships -- where users develop a deeper sense of connection with and attachment to the technology. This paper investigates what it means for relationships between users and advanced AI assistants to be appropriate and proposes a new framework to evaluate both users' relationships with AI and developers' design choices. We first provide an account of advanced AI assistants, motivating the question of appropriate relationships by exploring several distinctive features of this technology. These include anthropomorphic cues and the longevity of interactions with users, increased AI agency, generality and context ambiguity, and the forms and depth of dependence the relationship could engender. Drawing upon various ethical traditions, we then consider a series of values, including benefit, flourishing, autonomy and care, that characterise appropriate human interpersonal relationships. These values guide our analysis of how the distinctive features of AI assistants may give rise to inappropriate relationships with users. Specifically, we discuss a set of concrete risks arising from user--AI assistant relationships that: (1) cause direct emotional or physical harm to users, (2) limit opportunities for user personal development, (3) exploit user emotional dependence, and (4) generate material dependencies without adequate commitment to user needs. We conclude with a set of recommendations to address these risks.

Philosophers have recently put forward the possibility of achieving moral enhancement through artificial intelligence (e.g., Giubilini and Savulescu's version [32]), proposing various forms of "artificial moral advisor" (AMA) to help people make moral decisions without the drawbacks of human cognitive limitations. In this paper, we provide a new perspective on the AMA, drawing on empirical evidence from moral psychology to point out several challenges to these proposals that have been largely neglected by AI ethicists. In particular, we suggest that the AMA at its current conception is fundamentally misaligned with human moral psychology - it incorrectly assumes a static moral values framework underpinning the AMA's attunement to individual users, and people's reactions and subsequent (in)actions in response to the AMA suggestions will likely diverge substantially from expectations. As such, we note the necessity for a coherent understanding of human moral psychology in the future development of AMAs.

The new interdisciplinary field of AI ethics has revealed the extent to which AI systems tend to reflect back and amplify human vices: our unfair biases and discriminatory behaviours, our penchant for consuming and spreading misinformation, and our tendency to pursue narrow gains while losing sight of the bigger picture. While this is true, the mirror metaphor conveys the misleading and dangerous impression that AI merely captures and replicates our humanity in software. Yet we all know that a mirror does not capture the embodied human presence. Glass mirrors erase and occlude much of our material and conscious reality. Mirror images convey no smell, no depth, no softness, no fear, no hope, no imagination. What does the AI mirror occlude? In this talk I explore the dimensions of our humanity that AI's transformation of the socioeconomic and moral order makes it harder for us to see in ourselves and in one another, and why our futures depend upon bringing these vital aspects of our humanity back into view.

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. In ancient Greece, the basanos or touchstone had multiple meanings: a literal stone that tests the authenticity of gold by revealing its characteristic mark upon striking it, or metaphorically, a moral test of the authenticity of a life or a ruler. It also referred to a method of extracting truthful testimony by means of torture; specifically, of non-Greek slaves. The basanos thus embodies the interweaving of truth-telling with virtue, violence, and power in Western moral, political, and technical thought. In this talk I explore how contemporary uses of AI and data science have retraced and reconstituted the basanos in myriad ways, while also revealing a critical opportunity for the invention of new, more just and more sustainable means of truth-telling.

In this paper we argue that standard calls for explainability that focus on the epistemic inscrutability of black-box machine learning models may be misplaced. If we presume, for the sake of this paper, that machine learning can be a source of knowledge, then it makes sense to wonder what kind of \em justification it involves. How do we rationalize on the one hand the seeming justificatory black box with the observed wide adoption of machine learning? We argue that, in general, people implicitly adoptreliabilism regarding machine learning. Reliabilism is an epistemological theory of epistemic justification according to which a belief is warranted if it has been produced by a reliable process or method \citegoldman2012reliabilism. We argue that, in cases where model deployments require \em moral justification, reliabilism is not sufficient, and instead justifying deployment requires establishing robust human processes as a moral "wrapper'' around machine outputs. We then suggest that, in certain high-stakes domains with moral consequences, reliabilism does not provide another kind of necessary justification---moral justification. Finally, we offer cautions relevant to the (implicit or explicit) adoption of the reliabilist interpretation of machine learning.