Co-located with 23rd SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2017)
14 August 2017, Halifax, Nova Scotia, Canada
26 May 2017, 23:59 Anywhere on Earth (AoE)
This workshop aims to bring together a growing community of researchers, practitioners, and policymakers concerned with fairness, accountability, and transparency in machine learning. The past few years have seen growing recognition that machine learning raises novel ethical, policy, and legal challenges. In particular, policymakers, regulators, and advocates have expressed fears about the potentially discriminatory impact of machine learning and data-driven systems, 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 and opaqueness of data mining processes may reduce the justification for consequential decisions to "the algorithm made me do it" or "this is what the model says." 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. We seek contributions that attempt to measure and mitigate bias in machine learning, to audit and evaluate machine learning models, and to render such models more interpretable and their decisions more explainable.
This year, the workshop is co-located with the 23rd SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2017). The 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. This year, we place a special emphasis on papers describing how to bring tools for ensuring fairness, accountability, or transparency into real world applications of machine learning. We especially welcome submissions from practitioners in industry, government, and civil society.
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 will 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). We only wish to consider papers that have not yet been published elsewhere.
Papers should be submitted via Easy Chair.
16 June 2017
30 June 2017