FAT 2018 - Conference on Fairness, Accountability, and Transparency (FAT)

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 assymmetry 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.