This reflection explores the gap between explaining and understanding artificial intelligence’s (AI) outcomes, focusing on the example of AI-driven medical diagnostics. Explainable AI (XAI) is fundamentally rooted in machine learning and probabilistic methods, which is why its relationship to domain-specific contexts, such as medical diagnostics or policymaking, as well as to social concepts, such as trust or accountability, remains ambiguous. This creates a gap between what is technically considered as explaining and what, in turn, constitutes the understanding of a model’s predictions and its concrete, situated implications. In our contribution, we draw on the philosophy of science and its contextual notion of scientific understanding to highlight the need for explainable algorithmic outcomes that enable actors to both explain and intervene in creating, applying or interpreting knowledge. Our reflection highlights the potential tension between XAI as providing insights into an AI model’s technical operations and explainability as part of a situated understanding of how the model works within specific social contexts. We believe that bringing the concept of scientific understanding to empirical AI ethics can help us rethink explainability and contribute to more responsible ways of making sense of and interacting with black-box models, particularly in medical diagnostics.

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.

Mainstream artificial intelligence (AI) ethics primarily takes vertical approaches: top-down, principle-driven and expert-led frameworks that often exclude the very publics affected by algorithmic systems. This paper explores whether citizen juries (CJs) can function as a horizontal alternative empirical ethics method that emphasizes participation, situated knowledge and epistemic justice. We conducted a citizen jury with Brussels residents to deliberate on the use of an AI system for electricity distribution during brownouts. The jury process combined guidance ethics, speculative fiction, and generative AI tools to scaffold inclusive deliberation. Using affordance theory, we analyze how the citizen jury method structurally mediates certain forms of participation and ethical reasoning. We evaluate its normative value through the lenses of epistemic justice and effective performativity. Our findings show that citizen juries can foster testimonial inclusion and support hermeneutical sense-making, offering a partial corrective to abstract, generic AI ethics. However, we also identify critical constraints: problem-posing was narrowed, alternative epistemologies discouraged, and the outputs risked reproducing the generic performativity forms of vertical ethics. While citizen juries hold promise for doing AI ethics otherwise, their transformative potential hinges on reflexively examining their own methodological affordances and institutional framing.

Mainstream artificial intelligence (AI) ethics, with its reliance on top-down, principle-driven frameworks, often fails to account for the situated realities of diverse communities affected by AI. Critics have therefore argued that AI Ethics increasingly serves corporate interests through practices of “ethics washing,” operating as a tool for public relations rather than a means of preventing harm or advancing the common good. In response, this paper adopts a Science and Technology Studies perspective to interrogate AI Ethics itself as a matter of concern. This perspective brings into view a core tension between vertical approaches (external, top-down, principle-based) and horizontal approaches (from-within, risk-mitigating, implementation-oriented). By tracing how these models have shaped the discourse, we show their relevance, as well as their limitations. To move beyond these limitations, we propose a third model that integrates empirical research on local lifeworlds with explicit value commitments. We outline this empirical approach through four shifts: first, a transition from top-down reasoning toward empirical grounding; second, a transition from abstract principles to situated orders of worth; third, a proposal to operate across scales by attending to assemblages of concern; and fourth, a redirection from risk mitigation toward the cultivation of technologies of hope.

As artificial intelligence (AI) systems rapidly develop in biomedical research, bioethical frameworks focused on human subjects research are often limited in their capacity to address ethical concerns related to data-driven and black-boxed AI algorithms. Calls to embed Ethical, Legal and Social Implications research into large research consortia focused on AI seek to address the challenges of applying models developed in the context of genomics research to AI projects. Foundational questions related to the conduct of such embedded ethics work include: what are the goals of this work, what counts as “ethics,” who qualifies as an ethics expert and what mechanisms, including organizational structures, can ensure success in achieving ethics goals alongside scientific goals by multidisciplinary research teams? We draw on empirical case studies (including semi-structured interviews and document analysis) of large, federally funded AI research projects in the US that required embedded ethics. Notable findings include the lack of consensus among key players regarding the definition of AI ethics and conflict over the scope of ethics work, which impedes the implementation of ethics-related goals in research practices. These conflicts highlight the differences in disciplinary cultures, contentious boundaries between technical and ethical expertise and the lack of power and impact of the ethics team. Our findings underscore that effective ethics integration depends on trust and respect for ethics scholarship through leadership and relationship-building. We argue that building long-lasting, equitable relationships requires institutional commitment through organizational frameworks and funding mechanisms that prioritize deliberation about ethical concerns, shared values and shared power, as well as co-developing ethics goals alongside science goals. A horizontal approach is needed to nurture joint relationships for fostering ethical and just biomedical AI research and development.

Open Science has long been framed as a normative project grounded in values of transparency, accessibility and collaboration. Yet in the age of generative AI, many research data practitioners, who once championed openness, are increasingly questioning how these commitments can be upheld when openness also enables undesired large-scale data extraction, decontextualization and enclosure. This paper examines how the normativities of openness are being re-negotiated, and how researchers navigate the ethical and political tensions that arise when their work is repurposed for proprietary AI systems.

Drawing on in-depth qualitative interviews, I explore how situated ethical reasoning informs decisions about data sharing. Participants described new risks and dilemmas, from uncontrolled scraping and heightened re-identification threats to the reframing of FAIR (Findable, Accessible, Interoperable, Reusable) principles as pipelines for AI-readiness. Their accounts reveal openness as a relational and contested practice, embedded in specific institutional, infrastructural and community contexts, and continuously re-balanced between care, accountability and collaboration.

Rather than rejecting openness outright, interviewees described recalibrating it – through selective sharing, alternative infrastructures and advocacy for commons-based governance models.

Framed by critical data studies, and science and technology studies, the analysis conceptualizes these practices as reflexive agency: the situated capacity to align openness with evolving ethical and political conditions. The findings argue for re-politicizing openness as a matter of commons stewardship and situated ethics, aligning governance, infrastructure and normative commitments to resist extractive appropriation while sustaining the collective conditions for shared knowledge production.