Robots in the boardroom: incentivizing the optimal use of artificial intelligence by corporate directors

Parisi, Anna Clara Grace (A.A. 2021/2022) Robots in the boardroom: incentivizing the optimal use of artificial intelligence by corporate directors. Tesi di Laurea in Law and economics (corporate and business law: antitrust and regulation), Luiss Guido Carli, relatore Pierluigi Matera, pp. 40. [Bachelor's Degree Thesis]

Full text for this thesis not available from the repository.

Abstract/Index

Background. An operator, a victim and a manufacturer. Robots in the boardroom: legal challenges. Can algorithm be a director? A comparative analysis. Delegating to robots: breach or fulfillment of the fiduciary duty? The replacement of corporate directors by artificial intelligence. Artificial intelligence devices. Advancements in technology. Artificial intelligence devices in industry and businesses today. Industries that will be most affected by AI. DAOs. What is a DAO? How do DAOs work? Benefits of DAOs. Drawbacks of DAOs. The agency problem related to DAOs. The agency problem. How does the agency problem relate to DAOs? The business judgement rule. Fiduciary duties and the board of directors. Policy underlying the business judgement rule. The business judgement rule in United States law. The future business judgement rule. How artificial intelligence devices make business judgements. The business judgement rule applied to robots. A "dual liability" rule for corporate robots. Directors’ primary liability. Manufacturers residual liability. Economic effects of the "dual liability" rule. The price of the safer robot. Adoption of safer robots. Incentives to prove the directors’ negligence.

Thesis Type: Bachelor's Degree Thesis
Institution: Luiss Guido Carli
Degree Program: Bachelor's Degree Programs > Bachelor's Degree Program in Economics and Business, English language (L-33)
Chair: Law and economics (corporate and business law: antitrust and regulation)
Thesis Supervisor: Matera, Pierluigi
Academic Year: 2021/2022
Session: Autumn
Deposited by: Alessandro Perfetti
Date Deposited: 26 Jan 2023 15:28
Last Modified: 26 Jan 2023 15:28
URI: https://tesi.luiss.it/id/eprint/34784

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