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Nicolas Petit 27 september 18 - Hard Questions of Law and AI

The document discusses the legal and regulatory challenges posed by artificial intelligence (AI), emphasizing the need for a new framework due to inadequacies in existing laws regarding liability and ethical considerations. It contrasts ex ante and ex post regulation, highlighting issues such as the 'Collingridge Paradox' and the complexity of normative questions raised by AI applications like automated law enforcement. The paper advocates for a regulatory approach that balances both innovative technology and legal compliance to address existential risks and societal impact.

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HARD QUESTIONS OF LAW AND REGULATION OF ARTIFICIAL INTELLIGENCE (AI) Future Law – Budapest, 27 September 2018 Prof Nicolas Petit, ULiege (BE) and UniSA (AU)
www.lcii.eu  EU Commission High Level Expert Group on AI  Communication on AI  HLG: 2 subgroups  One tasked with drafting of ethical guidelines on AI  The other tasked with recommendations for policy an investment in AI  Through mid 2019 + EU AI Alliance: https://ec.europa.eu/futuriu m/en/eu-ai-alliance Appetite for regulation?  Parliament resolution, 16 Feb 2017, Civil Law Rules on Robotics  “Shortcomings” of legal framework  “questions whether the ordinary rules on liability are sufficient or whether it calls for new principles and rules to provide clarity on the legal liability”  Supports the “designation of a European Agency for Robotics and Artificial Intelligence in order to provide the technical, ethical and regulatory expertise needed to support the relevant public actors”
www.lcii.eu Hard questions of law & AI 1. AI in a nutshell 2. Ex ante v ex post regulation? 3. Normative issues: illustration from ALE 4. Ethics (before law): selected issues 5. Proposed framework: who, when and how to regulate?
www.lcii.eu 1. AI in a nutshell Core ideas  Any physical process including the mind process can be modelized as a computable algorithm (Church Turing thesis)  Machines can learn: “Learning is any process by which a system improves performance from experience” (Herbert Simon) Today  Brute force computational power now available (due to Moore’s law)  Zettabytes (or Yottabytes) of data now available, and distributed, and pre labelled (cloud)  « End of theory »  Deep learning, neural networks, etc.  Use cases: autonomous vehicles, predictive justice, predictive medicine, automated law enforcement
www.lcii.eu  Reactive  Mostly common law process  By courts  Standards (content of law unspecified ex ante)  Preemptive  Mostly civil law process  By technocratic or democratic  By expert or non expert organs  Rules (content of law specified ex ante) 2. Ex ante v ex post regulation of AI?  « Collingridge paradox »  Too early too late quandary?
www.lcii.eu Paradoxes of ex ante regulation Disciplinary approach  Lawyers need frictional case studies to apply deductive reasoning  One problem:  End up looking at sci-fi to generate case studies (EP, 2017)  Or end-up looking at the past to generate case studies  Techno-speculation: driverless cars not the dominant hypo in 1970s to 2000s Sci-Fi, but flying cars  Human-speculation: teleconferencing v conferencing  Risk of irrelevant law Technological approach  Emergent behavior hypothesis of AIs  Inevitable assumption that there are gaps in the legal system. New law is needed. Law of the horse.  Two problems:  Specificity of AI and robots emergent conduct is to emulate human level intelligence (Minsky, 1984) + emergent behavior relates to human conduct => existing law OK  Tendency “to attribute programs far more intelligence than they actually possess” (Yazdani, 1986)  Risk of redundant or extreme law
www.lcii.eu  Can the law ever be agile?  5 to 10 years to regulate tech that’s already 5 to 10 years old Paradoxes of ex post regulation  « Treacherous turn » (Bostrom)  Dominant design problem
www.lcii.eu 3. Normative issues  AI generates hard normative problems  « Should » questions
www.lcii.eu Case study: Automated Law Enforcement  “any computer-based system that uses input from unattended sensors to algorithmically determine that a crime has been, or is about to be, committed and then takes some responsive action, such as to prevent the crime to inform the appropriate law-enforcement agency or to impose some form of punishment” (Shay et al.)  Surveillance, analysis, aggregation and action  Police (telemetric policing) but other applications  UK: Connect (tax); US: Loomis case
www.lcii.eu Should « full enforcement » be the goal?  «Full enforcement » means perfect surveillance and punishment of infringements + perfect pre-emption  Legal: undercover criminality by law enforcement; civil disobedience; whistleblowing  Logical: paradox of disappearance of crime, and relaxation of social consensus against crime  Economics: imperfect enforcement is useful (Stigler, 1970)  Sociological: acceptability of technology is driven by context. Full enforcement more desirable in countries with endemic crime and corruption (Honduras v Greece)  Psychological: offender wishes to “to 'have his say' on the matter”
www.lcii.eu  Human dignity  Transparency and informed consent  Democracy  Autonomy and self- determination  Safety and Cybersecurity  Transparency, legibility and explainability  Non bias  Responsibility and accountability  Compliance with HRs  Red lines? But red rights? 4. Ethics  Artificial ethics: hardwired by design in robots  Natural ethics: rules that apply to researchers
www.lcii.eu Ongoing developments  Private ordering  Standardization: data practices certification  Unilateral products:  IBM just launched a software service that scans AI systems as they work in order to detect bias and provide explanations  Works with Watson tech, as well as Tensorflow, SparkML, AWS SageMaker, and AzureML  Public ordering: soft law and guidelines
www.lcii.eu 5. Proposed framework  Discrete externality (micro; personal)  Negative: harm  Positive: benefits  For harms, ex post resolution, abstract standards, impact assessment  Systemic externality (macro; societal)  Negative  Substitution effect in jobs  Privacy  Positive  Complementarity effect  Generative or General Purpose technologies  For harms, more ex ante, prescriptive, impact assessment  « Existernality »  Negative: existential risk (Terminator)  Positive: pure human enhancement  For harms, more ex ante, proscriptive, deontological (no impact assessment or fat tail risks) More at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2931339
Liege Competition and Innovation Institute (LCII) University of Liege (ULg) Quartier Agora | Place des Orateurs, 1, Bât. B 33, 4000 Liege, BELGIUM Thank you Nicolas.petit@uliege.be Twitter: @CompetitionProf