ACM Conference on Fairness Accountability and Transparency (CFAT) (CFAT)

We invite submissions for the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT). FAccT is an interdisciplinary conference dedicated to bringing together a diverse community of scholars advancing research in responsible, safe, ethical, and trustworthy computing. Research from all fields is welcome, including algorithmic, statistical, human-centered, theoretical, critical, legal, and philosophical research.

The 2025 conference will be held in Athens, Greece. Conference dates will be confirmed soon.

Subject Areas
FAccT welcomes papers that advance all areas related to the broad sociotechnical nature of computing, inviting work from computer science, engineering, the social sciences, humanities, and law.

Listed alphabetically, topics of interest include, but are not limited to:

AI red teaming and adversarial testing
Algorithmic fairness and bias
Algorithmic recourse
Appropriate reliance and trust in computational systems
Assurance testing and deployment policies
Audits of data, algorithms, models, systems, and applications
Critical and sociotechnical foresight studies of technologies, and related policies and practices
Cultural impacts of computational systems
Environmental impacts of computational systems
Fairness, accountability, and transparency in industry, government, or civic society
Historical, humanistic, social scientific, and cultural perspectives on FAccT issues
Human factors in fairness, accountability, and transparency
Intellectual property, privacy, data protection, antitrust, and mis/disinformation
Interdisciplinarity and cross-functional teaming in fairness, accountability, and transparency work
Interpretability/explainability
Justice, power, and inequality in computational systems
Labor and economic impacts of computational systems
Licensing and liability with AI
Moral, legal, and political philosophy of data and computational systems
Organizational factors in fairness, accountability, and transparency
Participatory and deliberative methods in fairness, accountability, and transparency
Regulation and governance of computational systems
Risks, harms, and failures of computational systems
Science of responsible, safe, ethical, and trustworthy AI evaluation and governance
Social epistemology of AI
Sociocultural and cognitive diversity in design and development
Sociotechnical design and development of data, models, and systems
Sociotechnical evaluations of data, models, and systems
Technical approaches to AI safety
Threat models and mitigations
Transparency documentation of data, models, systems, and processes
Value alignment and human feedback
Value-sensitive design of computational systems
Values in scientific inquiry and technology design as related to FAccT issues