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FAT* 2018 : 2018 Conference on Fairness, Accountability, and Transparency


When Feb 23, 2018 - Feb 24, 2018
Where New York City, USA
Abstract Registration Due Sep 29, 2017
Submission Deadline Oct 6, 2017
Notification Due Nov 17, 2017
Final Version Due Dec 17, 2017
Categories    machine learning   ethics   social sience   natural language processing

Call For Papers

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

Related Resources

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ACM-Ei/Scopus-ITNLP 2022   2022 2nd International Conference on Information Technology and Natural Language Processing (ITNLP 2022) -EI Compendex
MORS 2022   2nd workshop on multi-objective recommender systems
FAIML 2022   2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2022)
XKDD 2022   4th International Workshop on eXplainable Knowledge Discovery in Data Mining
FairWare 2022   FairWare'22: The International Workshop on Equitable Data and Technology