Gender violence encompasses a multitude of morally problematic psychological, physical, and sexual behaviors that, in most countries, constitute criminal offenses. In this study, we investigate the association between moral foundations (Care, Fairness, Loyalty, Authority, and Sanctity) and punitive responses to gender violence offenses. Our case study focuses on gender violence in Portugal, a country in which these offenses are a prevalent social problem. We collected data on gender violence legal cases decided in Portuguese courts between 2002 and 2022, and we used a latent class cluster analysis model to identify the complex patterns in the data and reduce such patterns to a distinct number of clusters. Four main clusters unravel latent relations between the foundations mapped in the legal narratives and corresponding punitive responses: (i) Affirmative with suspended prison time (moral rhetoric rooted in Authority); (ii) Mixed outcomes but no prison time (moral rhetoric rooted in Sanctity); (iii) Affirmative with lengthy prison time large compensation (moral rhetoric rooted in Loyalty and Care); and (iv) Affirmative with court fines (moral rhetoric rooted in Fairness). The moral foundations provide a valuable lens to understand the problem of gender violence, but further research is needed to establish the causal mechanisms between morality and punitive responses to gender violence.

The onset of autonomous driving has provided fertile ground for discussions about ethics in recent years. These discussions are heavily documented in the scientific literature and have mainly revolved around extreme traffic situations depicted as moral dilemmas, i.e. situations in which the autonomous vehicle (AV) is required to make a difficult moral choice. Quite surprisingly, little is known about the ethical issues in focus by the AV industry. General claims have been made about the struggles of companies regarding the ethical issues of AVs but these lack proper substantiation. As private companies are highly influential on the development and acceptance of AV technologies, a meaningful debate about the ethics of AVs should take into account the ethical issues prioritised by industry. In order to assess the awareness and engagement of industry on the ethics of AVs, we inspected the narratives in the official business and technical reports of companies with an AV testing permit in California. The findings of our literature and industry review suggest that: (i) given the plethora of ethical issues addressed in the reports, autonomous driving companies seem to be aware of and engaged in the ethics of autonomous driving technology; (ii) scientific literature and industry reports prioritise safety and cybersecurity; (iii) scientific and industry communities agree that AVs will not eliminate the risk of accidents; (iv) scientific literature on AV technology ethics is dominated by discussions about the trolley problem; (v) moral dilemmas resembling trolley cases are not addressed in industry reports but there are nuanced allusions that unravel underlying concerns about these extreme traffic situations; (vi) autonomous driving companies have different approaches with respect to the authority of remote operators; and (vii) companies seem invested in a lowest liability risk design strategy relying on rules and regulations, expedite investigations, and crash/collision avoidance algorithms.

As AI Systems become increasingly autonomous, they are expected to engage in decision-making processes that have moral implications. In this research we integrate theoretical and empirical lines of thought to address the matters of moral reasoning and moral uncertainty in AI Systems. We reconceptualize the metanormative framework for decision-making under moral uncertainty and we operationalize it through a latent class choice model. The core idea being that moral heterogeneity in society can be codified in terms of a small number of classes with distinct moral preferences and that this codification can be used to express moral uncertainty of an AI. Choice analysis allows for the identification of classes and their moral preferences based on observed choice data. Our reformulation of the metanormative framework is theory-rooted and practical in the sense that it avoids runtime issues in real time applications. To illustrate our approach we conceptualize a society in which AI Systems are in charge of making policy choices. While one of the systems uses a baseline morally certain model, the other uses a morally uncertain model. We highlight cases in which the AI Systems disagree about the policy to be chosen, thus illustrating the need to capture moral uncertainty in AI systems.