Law & Machine Learning

Céline Castets-Renard, Sylvain Cussat-Blanc, Laurent Risser

Keywords:  Law and AI    Law and machine learning    Social Justice and AI    Ethics and AI    Social and legal issues of AI    Fairness and AI    Privacy and AI    Explanability and AI    Interpretability and AI    Robustness and AI  


Description: the workshop proposal in “Law and Machine Learning” aims to contribute to the research on social and legal risks of the deployment of AI systems using machine learning based decisions. Today, algorithms have been infiltrating and governing every aspect of our lives as individuals and as a society. Specifically, Algorithmic Decision Systems (ADS) are involved in many social decisions. For instance, such systems are increasingly used to support decision-making in fields, such as child welfare, criminal justice, school assignment, teacher evaluation, fire risk assessment, homelessness prioritization, healthcare, Medicaid benefit, immigration decision systems or risk assessment, and predictive policing, among other things. Law enforcement agencies are increasingly using facial recognition, algorithmic predictive policing systems to forecast criminal activity and allocate police resources. However, these predictive systems challenge fundamental rights and guarantees of the criminal procedure. For several years, numerous studies have revealed, social risks of ML, especially the risks of opacity, bias, manipulation of information.

While it is only the starting point of the deployment of such systems, more interdisciplinary research is needed. Our purpose is to contribute to this new field which brings together legal researchers, mathematicians and computer scientists, by bridging the gap between the performance of algorithmic systems and legal standards. For instance, notions like “privacy” or “fairness” are formulated in law, as well as mathematical definitions in computer science. However, the meaning and the impact of such requirements are not necessarily identical. Besides, legal norms to regulate AI systems appear in certain national laws but have to be relevant and compatible with technical requirements. Furthermore, these standards must be checked by legal experts and regulators, which presupposes that AI systems are sufficiently meaningful and transparent. These issues emerge in different topics, such as privacy in data analysis and fairness in algorithmic decision-making. The topic will cover the research that denounces the risks and, above all, multidisciplinary research that proposes solutions, especially legal and technical solutions.

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Fri 2:00 a.m. - 2:15 a.m. [iCal]
Workshop presentation by the organizers (Workshop presentation) Céline Castets-Renard, Sylvain Cussat-Blanc, Laurent Risser
Fri 2:15 a.m. - 3:00 a.m. [iCal]
Professor Frederik Zuiderveen Borgesius (Amsterdam University & Radboud University): “Legal Protection in Europe against Discrimination by Machine Learning systems” (Keynote) Céline Castets-Renard, Sylvain Cussat-Blanc, Laurent Risser
Fri 3:00 a.m. - 4:15 a.m. [iCal]
  • 10:00-10:15: Algorithmic Recourse: from Counterfactual Explanations to Interventions (Amir-Hossein Karimi, Isabel Valera, Bernhard Schölkopf)
  • 10:15-10:30: Are AI-based Anti-Money Laundering (AML) Systems Compatible with European Fundamental Rights? (Astrid Bertrand, Winston Maxwell, Xavier Vamparys)
  • 10:30-10:45: Online publication of court records: circumventing the privacy-transparency trade-off (Tristan Allard, Louis Béziaud, Sébastien Gambs)
  • 10:45-11:00: The Gap between Deep Learning and Law: Predicting Employment Notice (Jason T. Lam, Rohan Bhambhoria, David Liang, Xiaodan Zhu, Samuel Dahan)
  • 11:00-11:15: Social Fairness, Accountability and Transparency of the Data Economy: Using Machine Learning to Combat the Emptiness of Privacy Policies (Przemysław Pałka, Roozbeh Yousefzadeh)
Fri 4:15 a.m. - 5:00 a.m. [iCal]
Live Q&A (Q&A) Céline Castets-Renard, Sylvain Cussat-Blanc, Laurent Risser
Fri 5:00 a.m. - 5:30 a.m. [iCal]
Lunch time (Break)
Fri 5:30 a.m. - 6:30 a.m. [iCal]

A virtual room was created to talk about each poster. Please go to the workshop website for the links

12:30-13:00: Live posters session 1 * Impact of Legal Requirements on Explainability in Machine Learning (Adrien Bibal, Michael Lognoul, Alexandre de Streel, Benoît Frénay) * Content Moderation as Legal Compliance: Annotating Hate Speech Using Judicial Legal Frameworks for Natural Language Processing Tasks (Thales Bertaglia, Giovanni De Gregorio, Catalina Goanta, Jerry Spanakis) * Predicting Court Decisions for Alimony: Avoiding Extra-legal Factors in Decision made by Judges and Not Understandable AI Models (Fabrice Muhlenbach, Isabelle Sayn, Long Nguyen-Phuoc) * Detecting and Explaining Unfairness in Consumer Contracts with Memory Networks (Federico Ruggeri, Francesca Lagioia, Marco Lippi, Paolo Torroni) * A Causal Linear Model to Quantify Edge Unfairness for Unfair Edge Prioritization and Discrimination Removal (Pavan Ravishankar, Pranshu Malviya, Balaraman Ravindran) * The interrelation between Data and AI Ethics in the context of Impact Assessments (Emre Kazim, Adriano Soares Koshiyama) - Poster - zoom link

13:00-13:30: Live posters session 2 * Accuracy Bounding: A Regulatory Path Forward for the Algorithmic Society (Aileen Nielsen) * The AI Accident Network: Artificial Intelligence Liability Meets Network Theory (Anat Lior) * Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices (Manish Raghavan, Solon Barocas, Jon Kleinberg, Karen Levy) * Conceptualizing Facial Recognition Technology in its Technical and Legal Dimensions (Natalia Menendez) * Facial Recognition: A cross-national Survey on Public Acceptance, Privacy, and Discrimination (Damian Borth, Lea Steinacker)

Fri 6:30 a.m. - 7:15 a.m. [iCal]
Professor Olivier Sylvain (Fordham Law School): “Recovering Tech's Humanity” (Keynote) Céline Castets-Renard, Sylvain Cussat-Blanc, Laurent Risser
Fri 7:15 a.m. - 7:45 a.m. [iCal]
Live Q&A (Q&A) Céline Castets-Renard, Sylvain Cussat-Blanc, Laurent Risser
Fri 7:45 a.m. - 9:00 a.m. [iCal]
  • 14:45-15:00: Regulating Accuracy-Efficiency Trade-Offs in Distributed Machine Learning Systems (A. Feder Cooper, Karen Levy, Christopher De Sa)
  • 15:00-15:15: Punishing Race, Poverty and Trauma: the Data behind predictive algorithms in the American justice system (Claire Boine, Jeffrey D. Krupa)
  • 15:15-15:30 : Legal Risks of Adversarial Machine Learning Research (Ram Shankar Siva Kumar, Jonathon Penney, Bruce Schneier, Kendra Albert)
  • 15:30-15:45: What Is a Proxy and Why Is It a Problem? (Margarita Boyarskaya, Solon Barocas, Hanna Wallach)
  • 15:45-16:00: Formalizing Data Deletion in the Context of the Right to be Forgotten (Sanjam Garg, Shafi Goldwasser, Prashant Nalini Vasudevan)
Fri 9:00 a.m. - 9:45 a.m. [iCal]
Live Q&A (Q&A) Céline Castets-Renard, Sylvain Cussat-Blanc, Laurent Risser
Fri 9:45 a.m. - 10:15 a.m. [iCal]
Live roundtable about the future of the workshop Law & Machine Learning (Panel) Céline Castets-Renard, Sylvain Cussat-Blanc, Laurent Risser