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

The first FAT* conference will be held February 23 and 24th, 2018 at New York University, NYC. 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.

Important Dates
Paper registration: September 29, 2017, 23:59 Anywhere on Earth (AoE)
Paper submission: October 6, 2017, 23:59 Anywhere on Earth (AoE)
Notification: November 17, 2017
Camera ready: December 17, 2017
Conference: February 23-24, 2018

Topics of Interest
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.

Further information is available at https://fatconference.org/2018/cfp.html


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