How should asylum seekers be distributed across EU member states? And can EU citizens agree on the principles underlying such distribution? While scholars have proposed various fairness principles – such as allocation based on population size, wealth, or past intake – we know little about which principles citizens support. Using an original population survey in six EU member states, this study examines public preferences for distributive fairness in asylum governance. We find widespread dissatisfaction with the status quo of large asymmetries in refugee numbers and broad support for European responsibility sharing based on fair distribution. However, there are significant differences between member states in the principles that citizens support, and these preferences are also affected by the principles that would keep the number of refugees in their own country at a minimum. These findings suggest that while there is a transnational consensus on the need to move to a fairer distribution of refugees in the EU, its practical operationalisation reveals the deeper political divides on the matter.

I argue against John, Millum and Wasserman’s position that telic prioritarianism justifies morally acceptable discrimination against persons with disabilities. I propose alternative considerations that explain why disability discrimination in the lifesaving cases JMW discuss is morally problematic.

Natural language processing (NLP) has moved from a specialized research field into the everyday infrastructure of writing, search, translation, education, journalism, public administration, and scientific work. This transition changes what counts as progress. Accuracy, fluency, and benchmark performance remain important, but they are no longer sufficient when language technologies shape knowledge, decisions, identities, and public trust. This column introduces Responsible NLP as a research orientation that integrates fairness, transparency, privacy, safety, cultural diversity, environmental awareness, and human agency across the full life cycle of language technologies. It argues that responsibility is not an external constraint on innovation, but a condition for meaningful and trustworthy innovation. Future research must therefore ask not only whether an NLP system works but also for whom it works, under which assumptions, with what risks, and with what forms of accountability.

Long-term unemployment (LTU) is a challenge for both jobseekers and public employment services. Statistical profiling tools are increasingly used to predict LTU risk. Some profiling tools are opaque, black-box machine learning (ML) models, which raise issues of transparency and fairness. The present paper investigates whether interpretable models could serve as an alternative, using administrative data from Switzerland. Traditional statistical, interpretable, and black-box models are compared in terms of predictive performance, interpretability, and fairness. It is shown that explainable boosting machines, a recent interpretable model, perform nearly as well as the best black-box models. It is also shown how model sparsity, feature smoothing, and fairness mitigation can enhance transparency and fairness with only minor losses in performance. These findings suggest that interpretable profiling provides an accountable and trustworthy alternative to black-box models without compromising performance.

Egalitarianism and prioritarianism are competing views about the ethics of distribution. Both views have wide scopes of concern. But, writing in this journal, Michael Otsuka has discovered a case that seems to show an interesting asymmetry between the limits of egalitarian and prioritarian concern. In that case, intuitively one outcome is better than another (in a respect relevant to the ethics of distribution); prioritarianism can recover that intuitive judgment; but egalitarianism seems unable to recover it. I show, however, that egalitarianism can recover the intuitive judgment in question on a well-motivated basis. That result is of interest because it shows the possibility and prima facie plausibility of a version of egalitarianism with a surprisingly wide scope of concern – one according to which it matters that some are worse off than those who (merely) could have existed.

The usual definitions of algorithmic fairness focus on population-level statistics, such as demographic parity or equal opportunity. However, in many social or economic contexts, fairness is not perceived globally, but locally, through an individual’s peer network and comparisons. We propose a theoretical model of perceived fairness networks, in which each individual’s sense of discrimination depends on the local topology of interactions. We show that even if a decision rule satisfies standard criteria of fairness, perceived discrimination can persist or even increase in the presence of homophily or assortative mixing. We propose a formalism for the concept of fairness perception, linking network structure, local observation, and social perception. Analytical and simulation results highlight how network topology affects the divergence between objective fairness and perceived fairness, with implications for algorithmic governance and applications in finance and collaborative insurance.

Artificial intelligence (AI) has the potential to help solve global problems and be employed “for good.” One area of immense recent investment and interest is the financial technology (“fintech”) sector. Boasting its ability to provide financial services for the underbanked, various startups are developing apps that collect mobile phone data and use machine learning (ML) to provide credit scores – and subsequently, opportunities to access loans – to groups often left out of traditional banking. Based on 25 semi-structured interviews with corporate leaders, data scientists and investors at fintech companies developing and managing ML-based alternative lending apps in low- and middle-income countries, this study delves into the different ways fintechs conceptualize and define fairness, including from both a process (“fair” algorithmic design) and outcome (“fair” credit assessment) perspective. By engaging insights from industry actors, this research reveals the cultural logics, power dynamics and institutional incentives that underpin fairness narratives, including how these dynamics shape gendered outcomes in access to credit. Rather than challenging systemic exclusion, fintechs prioritize scalability and profit over equity, developing and deploying “gender blind” algorithms that perpetuate and legitimize financial disparities under the guise of neutrality and objectivity. Ultimately, fairness in ML-driven lending is shaped by institutional priorities and economic logics, where fairness narratives inadvertently obscure systemic injustices even as ethical innovation is claimed. This paper contributes to the growing field of empirical AI ethics by examining how fairness is defined, contested and operationalized in fintech-driven algorithmic lending and the ensuing implications, before offering a feminist alternative to fairness in ML that centers community voices and sets equity as a normative horizon.

In this paper, I argue that Taurek’s positive view, namely that we ought to show equal respect and concern to those affected by our actions, commits him to saving the bigger number in some cases. This leads to an adjustment of his negative claim, namely that numbers don’t count. Numbers don’t count in the sense he was interested in, i.e., sums of harms or benefits (across different people) lack moral significance. Numbers do count, however, when considering how to act fairly which is what equal respect demands. This adjustment supports Taurek’s general view on how to think about moral matters.