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.