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FAT* 2018: Conference on Fairness, Accountability, and Transparency

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FAT*
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投稿締切日:
2017-09-29
通知日:
2017-11-17
開催日:
2018-02-23
開催地:
New York City, New York, USA
開催回数:
1
閲覧: 20558   フォロー: 0   参加: 0

会伴インデックス (CP-I)

35.8 / 100
全 5,682 件中 第 5,611 位 · 上位 99%
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%)
19
コミュニティの注目度 (10%)
8
公開情報の充実度 (15%)
25

使用した入力: 確認できる開催回数：1 · 過去 24 か月にこのページを開いた研究者：2 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 過去の開催回 (+3.0) · 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 1.1 · 算出日 2026-09-16

論文募集

FAT* 2018 (Conference on Fairness, Accountability, and Transparency) is an academic conference held in New York City, New York, USA on 2018-02-23. The paper submission deadline is 2017-09-29. Acceptance notifications are sent on 2017-11-17.

FAT* is an international and interdisciplinary peer-reviewed conference that seeks to publish and present work examining the fairness, accountability, and transparency of algorithmic systems. The FAT* conference solicits work from a wide variety of disciplines, including computer science, statistics, the humanities, and law. FAT* welcomes submissions that touch on any of the following topics (broadly construed): Fairness Techniques and models for fairness-aware data mining, information retrieval, recommendation, etc. Formalizations of fairness, bias, discrimination, etc. Translation of legal and ethical models of fairness into mathematical objectives User and experimental studies on perceptions of algorithmic bias and unfairness Design interventions to mitigate biases in systems, or discourage biased behavior from users Measurement and data collection regarding potential unfairness in systems Position and policy papers on how to design socially responsible and equitable systems Accountability Processes and strategies for developing accountable systems Methods and tools for ensuring that algorithms comply with fairness policies Metrics for measuring unfairness and bias in different contexts Techniques for guaranteeing accountability without necessitating transparency Techniques for ethical autonomous and A/B testing Privacy of user data Position and policy papers on the design and implementation of accountability regimes for systems Transparency Interpretability of machine learning models Generation of explanations for algorithmic outputs Design strategies for communicating the logic behind algorithmic systems User and experimental studies on the effectiveness of algorithm transparency techniques Tools and methodologies for conducting algorithm audits Empirical results from algorithm audits Frameworks for conducting ethical and legal algorithm audits This list of topics is not meant to be all-inclusive. Authors who are unclear about whether their work falls within the purview of the FAT* conference should contact the PC Chairs for clarification. Tracks To ensure that all submissions to FAT* are reviewed by a knowledgable and appropriate set of reviewers, the conference is divided into tracks. Authors must choose from the following tracks when they register their submissions: Theory and Security Statistics, Machine Learning, Data Mining, NLP, and Computer Vision Programming Languages, Databases, and other Systems (Recommender, Information Retrieval, etc.) Visualization, Human Computer Interaction, and User Studies Measurement and Algorithm Audits Law, Policy, and Social Science Archival and Non-archival FAT* 2018 offers authors the choice of archival and non-archival paper submissions. Archival papers will appear in the published proceedings of the conference, if they are accepted; conversely, accepted non-archival papers will only appear as abstracts in the proceedings. FAT* offers a non-archival option to avoid precluding the future submission of these papers to area-specific journals. Note that all submissions will be judged by the same quality standards, regardless of whether the authors choose the archival or non-archival option. Furthermore, reviewers will not be told whether submissions under review are archival or not, to avoid influencing their evaluations. Authors of all accepted papers must present their work at the FAT* 2018 conference, regardless of whether their paper is archival or non-archival.
最終更新：Dou Sun（）

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