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

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