Advances in machine learning and artificial intelligence mean that predictions and decisions of algorithms are already in use in many important situations under legal or regulatory control, and this is likely to increase dramatically in the near future. Examples include deciding whether to approve a bank loan, driving an autonomous car, or even predicting whether a prison inmate is likely to offend again if released.
This symposium will explore the key themes of privacy, transparency, accountability and fairness specifically as they relate to the legal treatment and regulation of algorithms and data. Our primary goals are (i) to inform our community about important current and ongoing legislation (e.g. the EU’s General Data Protection Regulation); and (ii) to bring together the legal and technical communities to help form better policy in the future.
We welcome machine learners, lawyers and anyone interested in social policy. Although the impact of machine learning on jobs in the legal profession is an important topic, that is not a key focus of this symposium.
Please note that registration has now closed.
Authors are invited to submit research abstracts on topics that relate broadly to the themes of machine learning and the law, including but not limited to issues of privacy, liability, transparency and fairness as they relate to algorithms and data.
Submissions should be up to 6 pages in NIPS format (short submissions are welcome, longer submissions may be accepted, please contact us if this would help you). Submissions need not be anonymized. Given the novelty of the field, we welcome a wide range of submissions, whether technical, legal or careful thought pieces to stimulate debate and discussion. We are happy to consider submissions that survey and comment on relevant work that has been previously published.
We aim to highlight a few submissions in spotlight presentations by authors at the symposium. All accepted papers will be made available on our symposium website, and will appear in an issue of JMLR Workshop and Conference Proceedings (unless authors prefer not).
Submission Deadline: Nov 3, 2016 [Deadline passed]
Decision to Authors: Nov 18, 2016
Final Papers Due: Dec 1, 2016 (papers may be revised following the symposium)
Please submit to firstname.lastname@example.org by Nov 3, 2016 (11:59PM PDT).
[***] Notable papers sponsored by Clifford Chance LLP and Leverhulme CFI
[***] B. Alarie, A. Niblett, A. Yoon, “Regulation by Machine” [pdf]
14:00 – 16:00
Legal perspectives: current legislation and upcoming challenges [session video]
Ian Kerr: Learned justice: prediction machines and big picture privacy
Mireille Hildebrandt: No free lunch [slides]
Deirdre Mulligan: Governance and Machine Learning: Challenges and Opportunities
16:00 – 16:30
16:30 – 18:30
Technical perspectives: privacy, transparency and fairness [session video]
Aaron Roth: Quantitative tradeoffs between fairness and accuracy in machine learning [slides]
Krishna P. Gummadi: Measures of fairness, and mechanisms to mitigate unfairness [slides]
Sara Hajian: Algorithmic bias: from measures of discrimination to methods of discovery [slides]
Yair Zick: Algorithmic Transparency & Quantitative Influence [slides]
18:30 – 19:30
19:30 – 21:00
Panel discussions [session video]
Panel 1. Ethics, impact and control of machine learning – risks and levers
Discussion spotlight: Benjamin Alarie: Regulation by machine? [slides]
Panel 2. Machine learning and the future of public policy
University of Washington
Carnegie Mellon University
University of Texas, Lucid AI
Krishna P. Gummadi
Eurecat-Tech Centre Catalonia
Queen Mary, University of London
University of Maryland
University of Cambridge
University of Pennsylvania