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.