Información de la conferencia

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

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Día de Entrega:
2026-10-27 Cerrado
Fecha de Notificación:
2027-03-23
Fecha de conferencia:
2027-06-21
Ubicación:
Porto, Portugal
Ediciones:
Vistas: 20923   Seguidores: 0   Asistentes: 0
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Índice Conference Partner (CP-I)

49,9 / 100
Puesto n.º 1.722 de 5.764 congresos · 30% superior
Reconocimiento académico (35%) Sin datos: se puntúa con la línea base neutra de 50 —
Selectividad en la revisión (20%) Sin datos: se puntúa con la línea base neutra de 50 —
Ediciones celebradas (20%)
65
Atención de la comunidad (10%)
12
Integridad del registro público (15%)
55

Datos utilizados: Ediciones documentadas: 10 · Investigadores que abrieron esta página en los últimos 24 meses: 4

Falta en el registro público: Tasas de aceptación históricas (+4,5) · Premios al mejor artículo (+2,3)
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Confianza 45 %: la parte de la puntuación respaldada por datos observados y no por la línea base neutra. Cómo se calcula esta puntuación · Ver la clasificación · Versión del algoritmo 1.2 · Calculado el 2026-10-31

Solicitud de Artículos

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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