ICML 2020 – Law and Machine Learning Workshop
Conférence internationale sur l'apprentissage automatique 2020
17 juill. 2020 — 8 h à 16 h
Présentation (en anglais seulement)
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. The purpose of this workshop 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 in applied mathematics and computer science. However, their meaning and their impact 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.
This workshop also aims to consider an AI regulatory framework. Specific legal rules have been enacted in the US, Europe, and Canada on algorithmic decision-making pursuing three different ways: certain norms grant rights, such as the right to be informed and the right not to be subject to a decision based solely on automated processing; other rules impose an algorithmic impact assessment before deploying ML systems; and, finally, other lawmakers have been established task forces to observe the impact of machine learning before adopting legal rules. Other sectoral regulations concern personal data protection, autonomous vehicles, biometric systems, or facial recognition. This workshop can make recommendations to the lawmakers, as well as lead researchers in machine learning to integrate legal requirements in the algorithms and in the developmental process of these algorithms, depending on the activity sectors of their applications. This workshop will bring together legal researchers, mathematicians, and computer scientists from the law and machine learning communities, and highlight recent work that contributes to addressing these challenges.
Organisé par (en anglais seulement)
- Céline Castets-Renard(University of Ottawa)
- Sylvain Cussat-Blanc (University of Toulouse)
- Laurent Risser (Mathematics Institute of Toulouse)
This workshop is sponsored by the Artificial and Natural Intelligence Toulouse Institute (3IA ANITI).