Conference Information

FAT* 2027: Conference on Fairness, Accountability, and Transparency

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Submission Date:
2026-10-27 Closed
Notification Date:
2027-03-23
Conference Date:
2027-06-21
Location:
Porto, Portugal
Years:
Viewed: 20927   Tracked: 0   Attend: 0
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Conference Partner Index (CP-I)

49.9 / 100
Ranked #1,722 of 5,764 conferences · Top 30%
Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
65
Community attention (10%)
12
Public record completeness (15%)
55

Inputs used: Editions on record: 10 · Researchers who opened this page in the past 24 months: 4

Missing from the public record: Historical acceptance rates (+4.5) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.2 · Computed 2026-10-31

Call For Papers

FAT* 2027 (Conference on Fairness, Accountability, and Transparency) is an academic conference held in Porto, Portugal on 2027-06-21. The paper submission deadline is 2026-10-27. Acceptance notifications are sent on 2027-03-23.

Topics of Interest Listed alphabetically, topics of interest include, but are not limited to: AI red teaming and adversarial testing Algorithmic fairness and bias Algorithmic recourse (In)appropriate reliance and (over)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 Diversity 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 systems Fairness, accountability, and transparency in industry, government, or civil society Historical, humanistic, social scientific, and cultural perspectives on topics in this list Human-centered approaches to factors in fairness, accountability, and transparency 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 Legal topics in AI (e.g., antitrust, bias and discrimination, data protection, intellectual property, mis/disinformation, and privacy) 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 Resistance, refusal, and contestation of computational systems Responsible data management and data engineering Risks, harms, and failures of computational systems Science of responsible, safe, ethical, and trustworthy AI evaluation and governance Social epistemology of AI Sociotechnical design and development of data, models, and systems Sociotechnical evaluations of data, models, and systems Sociotechnical approaches to AI safety Threat models and mitigations Transparency documentation of data, models, systems, processes, and outcomes Value alignment and human feedback Value-sensitive design of computational systems Values in scientific inquiry and technology design as related to FAccT issues Topics out of scope: FAccT is an interdisciplinary conference striving to impact and shape real-world socio-technical issues. We welcome submissions with varying methodologies, epistemologies, and disciplinary orientations that all seek to address that aim. ​However, work that does not have deep engagement with the social components of computational systems or that is focused on purely hypothetical concerns is considered outside the scope of the conference.
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