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

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投稿締切日:
2026-10-27 締切済み
通知日:
2027-03-23
開催日:
2027-06-21
開催地:
Porto, Portugal
開催回数:
閲覧: 20924   フォロー: 0   参加: 0
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FAT*
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会伴インデックス (CP-I)

49.9 / 100
全 5,764 件中 第 1,722 位 · 上位 30%
学術的評価 (35%) データなし — 中立の基準値 50 点として算入 —
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
65
コミュニティの注目度 (10%)
12
公開情報の充実度 (15%)
55

使用した入力: 確認できる開催回数：10 · 過去 24 か月にこのページを開いた研究者：4 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 45% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.2 · 算出日 2026-10-31

論文募集

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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