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FATM 2016 - 3rd Workshop on Fairness, Accountability, and Transparency in Machine Learning

Date2016-11-18

Deadline2016-09-15

VenueNew York, NY, USA - United States USA - United States

Keywords

Websitehttp://fatml.org

Topics/Call fo Papers

3rd Workshop on Fairness, Accountability, and Transparency in Machine Learning
Co-located with the Data Transparency Lab 2016
November 18, New York, NY
http://fatml.org/
Submission Deadline: September 9, 2016
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OVERVIEW
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This workshop aims to bring together a growing community of researchers and practitioners concerned with fairness, accountability, and transparency in machine learning. The past few years have seen growing recognition that machine learning raises novel challenges for ensuring non-discrimination, due process, and understandability in decision-making. In particular, policymakers, regulators, and advocates have expressed fears about the potentially discriminatory impact of machine learning, with many calling for further technical research into the dangers of inadvertently encoding bias into automated decisions. At the same time, there is increasing alarm that the complexity of machine learning may reduce the justification for consequential decisions to “the algorithm made me do it.” The goal of this workshop is to provide researchers with a venue to explore how to characterize and address these issues with computationally rigorous methods.
This year, the workshop is co-located with two other highly related events: the Data Transparency Lab (DTL) Conference and the Workshop on Data and Algorithmic Transparency (DAT). We anticipate that our workshop will consist of a mix of invited talks, invited panels, and contributed talks. We welcome paper submissions that address any issue of fairness, accountability, and transparency related to machine learning, especially those that provide a bridge to empirical studies of the behavior of data-driven systems in the wild, the focus of the DTL and DAT events.
TOPICS OF INTEREST
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Fairness:
• Can we develop new computational techniques for discrimination-aware data mining? How should we handle, for example, bias in training data sets?
• How should we formalize fairness? What does it mean for an algorithm to be fair?
• Should we look only to the law for definitions of fairness? Are legal definitions sufficient? • Can legal definitions even be translated to practical algorithmic contexts?
• Can we develop definitions of discrimination and disparate impact that move beyond the Equal Employment Opportunity Commission’s 80% rule?
• Who decides what counts as fair when fairness becomes a machine learning objective?
• Are there any dangers in turning questions of fairness into computational problems?
Accountability:
• What would human review entail if models were available for direct inspection?
• Are there practical methods to test existing algorithms for compliance with a policy?
• Can we prove that an algorithm behaves in some way without having to reveal the algorithm? Can we achieve accountability without transparency?
• How can we conduct reliable empirical black-box testing and/or reverse engineer algorithms to test for ethically salient differential treatment?
• What are the societal implications of autonomous experimentation? How can we manage the risks that such experimentation might pose to users?
Transparency:
• How can we develop interpretable machine learning methods that provide ways to manage the complexity of a model and/or generate meaningful explanations?
• Can we use adversarial conditions to learn about the inner workings of inscrutable algorithms? Can we learn from the ways they fail on edge cases?
• How can we use game theory and machine learning to build fully transparent, but robust models using signals that people would face severe costs in trying to manipulate?
PAPER SUBMISSION
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Papers are limited to 4 content pages, including figures and tables, and should use a standard 2-column 11pt format; however, an additional fifth page containing only cited references is permitted. Papers must be anonymized for double-blind reviewing. Accepted papers will be made available on the workshop website and should also be posted by the authors to arXiv; however, the workshop's proceedings can be considered non-archival, meaning that contributors are free to publish their work in archival journals or conferences. Accepted papers will be either presented as a talk or poster (to be determined by the workshop organizers).
Papers should be submitted here:
https://easychair.org/conferences/?conf=fatml2016
Complete Paper Submissions Due: September 9, 2016, 11:59:59PM EDT
Notification to Authors: October 7, 2016
Camera-Ready Papers Due: October 28, 2016
RELATED CALL
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Authors of especially well developed papers should also consider submitting to a special issue of Big Data on “Social and Technical Trade-Offs,” which is being guest edited by a number of the workshop organizers: http://www.liebertpub.com/lpages/big-data-cfp-soci...
Complete Manuscript Submissions Due: September 15, 2016
ORGANIZATION
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Workshop Organizers:
Solon Barocas, Microsoft Research NYC
Sorelle Friedler, Haverford College
Moritz Hardt, Google
Joshua Kroll, CloudFlare
Suresh Venkatasubramanian, University of Utah
Hanna Wallach, Microsoft Research NYC
Program Committee:
Sorelle Friedler, Haverford College, Co-Chair
Suresh Venkatasubramanian, University of Utah, Co-Chair
Bettina Berendt, KU Leuven
Anupam Datta, Carnegie Mellon University
Hal Daume, University of Maryland, College Park
Fernando Diaz, Microsoft Research NYC
Krishna Gummadi, MPI-SWS
Sara Hajian, Eurecat, Technology Center of Catalonia
Kristian Lum, Human Rights Data Analysis Group
David Robinson, Upturn
Salvatore Ruggieri, Università di Pisa
Julia Stoyanovich, Drexel University
Christo Wilson, Northeastern University

Last modified: 2016-08-12 23:23:43