Minds and Machines (2021)
AbstractA series of recent developments points towards auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics. Building on ongoing discussions concerning ethics-based auditing, we offer three contributions. First, we argue that ethics-based auditing can improve the quality of decision making, increase user satisfaction, unlock growth potential, enable law-making, and relieve human suffering. Second, we highlight current best practices to support the design and implementation of ethics-based auditing: To be feasible and effective, ethics-based auditing should take the form of a continuous and constructive process, approach ethical alignment from a system perspective, and be aligned with public policies and incentives for ethically desirable behaviour. Third, we identify and discuss the constraints associated with ethics-based auditing. Only by understanding and accounting for these constraints can ethics-based auditing facilitate ethical alignment of AI, while enabling society to reap the full economic and social benefits of automation.
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Citations of this work
Ethics-based auditing of automated decision-making systems: nature, scope, and limitations.Jakob Mökander, Jessica Morley, Mariarosaria Taddeo & Luciano Floridi - 2021 - Science and Engineering Ethics 27 (4):1–30.
The European Legislation on AI: A Brief Analysis of its Philosophical Approach.Luciano Floridi - 2021 - Philosophy and Technology 34 (2):215–222.
Transparency and the Black Box Problem: Why We Do Not Trust AI.Warren J. von Eschenbach - 2021 - Philosophy and Technology 34 (4):1607-1622.
Ethical Principles for Artificial Intelligence in National Defence.Mariarosaria Taddeo, David McNeish, Alexander Blanchard & Elizabeth Edgar - 2021 - Philosophy and Technology 34 (4):1707-1729.
Conformity Assessments and Post-Market Monitoring: A Guide to the Role of Auditing in the Proposed European AI Regulation.Jakob Mökander, Maria Axente, Federico Casolari & Luciano Floridi - 2022 - Minds and Machines 32 (2):241-268.
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A United Framework of Five Principles for AI in Society.Luciano Floridi & Josh Cowls - 2019 - Harvard Data Science Review 1 (1).