Machine Learning
and the Law

NIPS Symposium
8 December, 2016
Barcelona, Spain 

Symposium videos now available

Symposium overview

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. 


(Speakers and panelists)

Solon Barocas

Microsoft Research

Kay Firth-Butterfield

University of Texas, Lucid AI

Sara Hajian

Eurecat-Tech Centre Catalonia

Mireille Hildebrandt

Vrije Universiteit Brussel

Neil Lawrence

University of Sheffield

Deirdre Mulligan

UC Berkeley

Call for papers

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 by Nov 3, 2016 (11:59PM PDT).

Accepted papers

[***] Notable papers sponsored by Clifford Chance LLP and Leverhulme CFI

[***] B. Alarie, A. Niblett, A. Yoon, “Regulation by Machine” [pdf]  

S. Albanie, “Unknowable Manipulators: Regulation of Curation in Social Networks” [pdf]

T. Burri, “Machine Learning and the Law: Five Theses” [pdf]

W. Campbell, L. Li, C. Dagli, K. Greenfield, E. Wolf, J. Campbell, “Predicting and Analyzing Factors in Patent Litigation” [pdf]

D. Chen, X. Cui, L. Shang, J. Zheng, “What Matters: Agreement between US Courts of Appeals Judges” [n/a]

B. Goodman, “A Step Towards Accountable Algorithms?: Algorithmic Discrimination and the European Union General Data Protection”  [pdf]

[***] B. Goodman, “Computer Says No: Economic Models of (Algorithmic) Discrimination” [pdf] 

[***] N. Grgic-Hlaca, M. Zafar, K. P. Gummadi, A. Weller, “The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making” [pdf] ***

D. Kamarinou, C. Millard, J. Singh, “Machine Learning with Personal Data: Profiling, Decisions and the EU General Data Protection Regulation” [pdf]

H. Lakkaraju, C. Rudin, “Learning Cost-Effective and Interpretable Treatment Regimes for Judicial Bail Decisions” [pdf]

J. Singh, “The tech-legal aspects of machine learning: Areas for moving forward” [pdf]


Thursday 8th of December, 2016

[[ Topics for the panel Q&A ]]
Panel 1: Near-term issues  
 Panel 2: Long-term issues

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

Coffee break

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

Dinner break

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


Conrad McDonnell

Gray's Inn Chambers

Programme committee

Solon Barocas

Microsoft Research

Ryan Calo

University of Washington

Anupam Datta

Carnegie Mellon University

Kay Firth-Butterfield

University of Texas, Lucid AI

Krishna P. Gummadi


Sara Hajian

Eurecat-Tech Centre Catalonia

Daniel Hsu

Columbia University

Frank McSherry

Microsoft Research

Christopher Millard

Queen Mary, University of London

Deirdre Mulligan 

UC Berkeley

Frank Pasquale

University of Maryland

Julia Powles

University of Cambridge

Aaron Roth

University of Pennsylvania

Andrew Selbst

Georgetown University


The support of the following organisations is gratefully acknowledged.

Centre for the Study of Existential Risk

Clifford Chance

Clifford Chance LLP


Leverhulme Centre for the Future of Intelligence 


The Alan Turing Institute