Important dates

All deadlines are Anywhere on Earth (AoE).

About the workshop

Over the last decade, artificial intelligence (AI) systems have been increasingly deployed in the healthcare domain and have become commonplace within conversations around health. Our focus is on AI deployed in health and care settings - systems that enter diagnosis, treatment, and the work of healthcare providers, while remaining attentive to the wider conditions of health in which these systems operate. As AI systems rapidly reshape the healthcare environment, meaningful impact, not merely good performance, becomes the critical concern. This has led to a growth in research pertaining to, among others, explainable and trustworthy AI. Nevertheless, many existing AI systems still fail to deliver on their promises in healthcare workflows and other real-world use cases, often because legal, ethical, and epistemic challenges have not been integrated into their development and deployment.

Furthermore, AIES provides a groundwork for sharing research across computer and social sciences, law and policy, ethics, and philosophy. Within each of these domains, AI systems for health are developed and evaluated under different definitions of accountability, ethics, trustworthiness, safety, and compliance. The result is that each domain remains siloed from the others, with little articulation across the health AI field. Yet this articulation is essential for the development and evaluation of real-world AI systems. We count ourselves within this condition rather than outside it: as organizers drawn from computer science, social science, law, ethics, and philosophy, we recognize that the meanings we attach to terms such as accountability, trust, safety, and compliance are themselves shaped by the disciplines that trained us. These are, in a precise sense, essentially contested concepts (Gallie, 1956), and the divergence among our usages is a structural feature of cross-disciplinary work rather than a failure of rigour. We do not assume that a single shared definition can or should be imposed. Instead, we offer this workshop as a trading zone (Galison, 1997) in which differing disciplinary understandings can be surfaced, held side by side, and articulated. What we seek is the convergence of perspectives, understood as an achievement to be worked toward, not a premise to be assumed.

To this end, the Converging Perspectives on Health AI Workshop aims to provide a space for community building centered on the question: How can we develop healthcare AI systems that are clinically useful, technically robust, socially acceptable, and responsibly deployed? Or, put simply, how should we be using or not using AI in real-world clinical settings?

We especially encourage submissions that:

• are grounded in real clinical deployments, health-system settings, or concrete use cases
• connect normative or ethical claims to specific sociotechnical, organizational, legal, or regulatory contexts in health
• offer cross-national or comparative perspectives across health systems
• provide reflexive accounts of ethics-in-practice, from the clinic to the data pipeline
• speak clearly and substantively across disciplinary boundaries

References

Gallie, W. B. (1955, January). Essentially contested concepts. In Proceedings of the Aristotelian society (Vol. 56, pp. 167-198). Aristotelian Society, Wiley.

Galison, P. (1997). Image and logic: A material culture of microphysics. University of Chicago Press

Themes of Interest

The list is illustrative, not exhaustive. The groupings name shared problems, not disciplines; most contributions will speak to more than one, and we particularly welcome work that does.

Trust, Evidence, and Evaluation

• Defining, measuring, and contesting trust and trustworthiness across the fields that build and study health AI
• Evaluation frameworks beyond accuracy, i.e., safety, usability, robustness, and downstream clinical outcomes
• Communication of uncertainty, model limitations, and failure modes to healthcare providers, patients, and the public
• Standards of evidence for health AI, and what counts as proof of benefit across disciplines

Deployment, Translation, and Practice

• Translational gaps between research performance and real-world use
• Clinician-centered design, human-AI interaction, and integration into health and care workflows
• Organizational, institutional, and cultural conditions shaping adoption, non-adoption, and resistance
• Lessons from other high-stakes domains (e.g., aviation, nuclear, finance) on safety and oversight in practice

Governance, Regulation, and Accountability

• Regulatory and governance frameworks for health AI, statutory regimes, soft law, and technical standards (e.g., GDPR, EU AI Act, EHDS, MDR, HIPAA, FAIR, ISO), including comparative and international perspectives
• Data governance: access, sharing, and reuse of health data across development and deployment
• Accountability, liability, and meaningful human oversight across the health AI value chain — developers, deployers, and healthcare providers
• Post-deployment harm, redress, and mechanisms for contesting automated decisions in care

Justice, Rights, and the Politics of Health AI

• Fundamental rights, non-discrimination, and the fair distribution of benefits and burdens
• Equity and access in health AI — fair performance across patient populations and the distribution of benefits and burdens in care
• Participatory and community-informed approaches to building and evaluating health AI
• Critique, refusal, and the limits of AI in health — where it should not be deployed at all

Sample workshop schedule

Submission Guidelines

• Accepted papers will be included in the workshop proceedings.
• Submitted papers may be at most 4 pages (including abstract, figures and tables) in the 2-column style of the AAAI-2026 Author Kit, see https://aaai.org/authorkit26-1/.
• Authors may use an unlimited number of pages for references.
• All submissions must be made through the EasyChair link here. All submissions will be reviewed single blind.
• At least one author on every paper submission should sign up as a reviewer here: https://forms.gle/NtXUSH4b2wzBkMHH8.
• Authors may provide, but are not required, an optional ethical considerations statement, researcher positionality statement, and adverse impact statement. For an overview of positionality statements, please see (https://www.youtube.com/watch?v=GpcIVzGYhVs) and for adverse impact statements see (https://medium.com/@alexandra.olteanu/responsible-ai-research-needs-impact-statements-too-7b7141031faf). These statements may be given on one extra page at the end of the paper before references. This page will not count toward the overall page count. These sections are not meant for the discussion of methodological limitations, which should be addressed in the main paper.
• Note that papers that do not follow the above formatting guidelines will be desk-rejected.
• All materials are due on the paper deadline above.

Workshop and Programme Chairs

Organizing Committee

FAQ