Conference on February 23-24, 2018 in New York City, New York.
A two-day event that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems. This inaugural conference builds on success of prior workshops like FAT/ML, FAT/Rec, DAT, Ethics in NLP, and others.
Algorithmic systems are being adopted in a growing number of contexts. Fueled by big data, these systems filter, sort, score, recommend, personalize, and otherwise shape human experiences of socio-technical systems. Although these systems bring myriad benefits, they also contain inherent risks, such as codifying and entrenching biases; reducing accountability and hindering due process; and increasing the information asymmetry between data producers and data holders.
FAT* is an annual conference dedicating to bringing together a diverse community to investigate and tackle issues in this emerging area. Topics of interest include, but are not limited to:
- The theory and practice of fair and interpretable Machine Learning, Information Retrieval, NLP, and Computer Vision
- Measurement and auditing of deployed systems
- Users’ experience of algorithms, and design interventions to empower users
- The ethical, moral, social, and policy implications of big data and ubiquitous intelligent systems
FAT* builds upon several years of successful workshops on the topics of fairness, accountability, transparency, ethics, and interpretability in machine learning, recommender systems, the web, and other technical disciplines.
Historical Note: In 2018, the conference’s name was FAT* and the proceedings were published in the Journal of Machine Learning Research. The conference affiliated with ACM in 2019, and changed its name to ACM FAccT immediately following the 2020 conference.