Archival papers
Accepted papers appear in the published conference proceedings in the ACM Digital Library, considered equivalent in rigor to journal publications.
AI/CORE Unranked
ACM FAccT 2027
The Tenth Annual ACM Conference on Fairness, Accountability, and Transparency
ACM FAccT 2027 is an interdisciplinary conference focused on fairness, accountability, and transparency in socio-technical systems, bringing together researchers from computer science, law, social sciences, and humanities. It aims to address the ethical, societal, and technical challenges posed by algorithmic systems in areas such as decision-making, governance, and human-computer interaction.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Non-archival papers
Accepted papers appear only as abstracts in the proceedings but are presented at the conference; authors may later submit to discipline-specific journals or law reviews.
Research areas in scope
01
Focus Areas
Evaluations and evaluation practicesExperiences and interactionsLaw and policyNormative foundations and implicationsPower and practiceSystem development and deployment
02
Topics of Interest
AI red teaming and adversarial testingAlgorithmic fairness and biasAlgorithmic recourse(In)appropriate reliance and (over)trust in computational systemsAssurance testing and deployment policiesAudits of data, algorithms, models, systems, and applicationsCritical and sociotechnical foresight studies of technologies, and related policies and practicesCultural impacts of computational systemsDiversity in design and development (i.e., diversity as defined along many possible dimensions, such as sociocultural, demographic, ability-based, and more)Environmental impacts of computational systemsFairness, accountability, and transparency in industry, government, or civil societyHistorical, humanistic, social scientific, and cultural perspectives on topics in this listHuman-centered approaches to factors in fairness, accountability, and transparencyInterdisciplinarity and cross-functional teaming in fairness, accountability, and transparency workInterpretability/explainabilityJustice, power, and inequality in computational systemsLabor and economic impacts of computational systemsLegal topics in AI (e.g., antitrust, bias and discrimination, data protection, intellectual property, mis/disinformation, and privacy)Licensing and liability with AIMoral, legal, and political philosophy of data and computational systemsOrganizational factors in fairness, accountability, and transparencyParticipatory and deliberative methods in fairness, accountability, and transparencyRegulation and governance of computational systemsResistance, refusal, and contestation of computational systemsResponsible data management and data engineeringRisks, harms, and failures of computational systemsScience of responsible, safe, ethical, and trustworthy AI evaluation and governanceSocial epistemology of AISociotechnical design and development of data, models, and systemsSociotechnical evaluations of data, models, and systemsSociotechnical approaches to AI safetyThreat models and mitigationsTransparency documentation of data, models, systems, processes, and outcomesValue alignment and human feedbackValue-sensitive design of computational systemsValues in scientific inquiry and technology design as related to FAccT issues
Policies worth checking twice
- Submissions must be anonymized with no identifying information, including names, affiliations, acknowledgements, or third-person citations of prior work by the authors.
- Papers must not exceed 14 pages (excluding references); an additional page is allowed for ethics, adverse impacts, and other endmatter statements.
- Revised papers accepted for revision may include up to 15 pages (excluding references and endmatter).
- Concurrent or dual submission to other peer-reviewed conferences or journals is prohibited, with exceptions for preprints, non-archival workshop papers, and journal submissions under non-archival FAccT option.
- Authors must include a generative AI usage statement in the endmatter, disclosing any use of AI tools in writing the paper, even if none was used.
- Authors must sign up to review for the conference; failure to do so may lead to desk rejection.
- At least one author of each accepted paper must register and present the work at the conference, either in person or virtually.
- Submissions must follow ACM TAPS formatting guidelines using LaTeX or Word templates, with single-column layout and anonymous review settings.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026