Leading legal disruption : artificial intelligence and a toolkit for lawyers and the law / [edited by] Giuseppina (Pina) D'Agostino, Aviv Gaon, Carole Piovesan.
Table of cases. Acknowledgements -- Table of contents -- Foreword / Vivek Krishnamurthy -- Artificial Intelligence and human rights: a contested relationship / Giuseppina (Pina) D'Agostino, Aviv Gaon, and Carole Piovesan -- 1. Artificial Intelligence and ( the lack of) security / Oleg Brodt, Michael Khavkin, Lior Rokach, Asaf Shabtai, and Yuval Elovici -- 2. AI and legal scholarship: reflections on evolution and influences / Jonathon W. Penney -- 3. Intellectual property strategy for AI technology in Canada / Maya Medeiros, William Chalmers, and Jordana Sanft -- 4. Inventive machines and obvious(ness) legal disruptions / Ryan Abbott -- 5. Legal issues around data scraping as a source of data for AI innovation / Teresa Scassa -- 6. Ethical lawyering and Artificial Intelligence / Ian Stedman -- 7. Artificial Intelligence and damages: assessing liability and calculating damages / Yaniv Benhamou and Justine Ferland -- 8. When AI Systems Are Negligent / Kami Chagal-Feferkorn -- 9. Smarter contracts: AI and smart contracts / Gregory R. Hagen -- 10. The meeting of the new minds: contract law, intent and Artificial Intelligence / Salil K. Mehra -- 11. The introduction of medical algorithms into the reimbursement process: incentivizing industry-payer collaboration / Maya Peleg, Shai Yom-Tov, Dor Nahshoni, and Dov Greenbaum -- 12. The rise of fintech: promises, perils, and challenges / Moran Ofir and Ido Sadeh -- 13. Identifying the potential and risks of AI integration to taxation: the case of general anti-avoidance rule / Blazej Kuzniacki and Kamil Tylinski -- 14. Artificial Intelligence and regulation / Anthony Niblett -- 15. Regulating Artificial Intelligence in the European Union / Jan De Bruyne and Brahim Benichou -- 16. Artificial Intelligence governance and policy: a practical guide to identifying, understanding, and mitigating legal risks associated with AI integration / Anjanette H. Raymond, Chris Draper, and Dakota Coates -- 17. Artificial Intelligence and human rights: a contested relationship / Vivek Krishnamurthy -- Table of cases.
Summary:
Establishing liability for damages caused by AI used to be rather straightforward when only one or few stakeholders are involved, or when the AI could only take a limited range of pre-defined decisions in accordance with specific parameters defined by a human programmer. However, AI usually involves several stakeholders and components (e.g. sensors and hardware, softwares and applications, data itself and data services, connectivity features) and recent forms of AI are increasingly able to learn without human supervision which makes it difficult to allocate liability between all stakeholders. This contributions maps various possibilities, identify their challenges and explore lines of thought to develop new solutions or close the gaps, the whole from a global perspective. Existing liability regimes already offer basic protection of victims, to the extent that specific characteristics of emerging technologies are taken into account. Consequently, instead of considering new liability principles (solutions that require certain amendments of the current liability regimes), one should consider simply adapting current fault-based liability regimes with enhanced duties of care and precisions regarding shared liability and solidarity between tortfeasors, which could potentially be done through case-law in most jurisdictions. When it comes to the calculation of damages, given the difficulties in calculating the damage and to take into account the specificities of IPR or privacy rights, economic methods may be considered to calculate the damages in general, such as the Discounted Cash Flow Method (DCF) and the Financial Indicative Running Royalty Model (FIRRM), as well as the Royalty Rate Method and case-law about Fair, Reasonable and Non-Discriminatory license terms (FRAND). This path will lead to a certain "flat-rating"damages ("baremisation" or "forfaitisation"), at least when IPR and personal data are illegally used by AI-tools and mostly not visible, hence barely quantifiable in terms of damages. -- Provided by publisher.
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