Ethics and the Regulation of Artificial Intelligence Fairness, Accuracy, Transparency, and Knowability

This edited volume explores the critical intersection between fairness, accuracy, knowability, and transparency in national and EU regulatory frameworks on Artificial Intelligence (AI). Designed to prevent harms such as violations of fundamental rights, these frameworks have nonetheless faced significant challenges in ensuring the practical detectability of the harms they seek to address, as well as in effectively promoting transparency and fairness within the EU digital market
The contributions to this edited volume offer critical and insightful perspectives on those challenges through the analytical lens of a conceptual quadrant - fairness, transparency, accuracy and knowability - that underpins this volume’s three parts.
Part I establishes the conceptual groundwork, reframing fairness as contingent upon the epistemic quality of decision-making procedures. Part II examines how these values are operationalized within frameworks like the EU’s AI Act (AIA), highlighting regulatory loopholes in remote biometric identification and transparency exceptions. Part III focuses on individual entitlements, exploring the right to an explanation, and the protection of vulnerable groups in digital justice systems. Ultimately, this edited volume argues that formal compliance with AI regulation is insufficient to safeguard fundamental rights, and advocates for a structural governance framework capable of correcting power imbalances and effectively protecting citizens from opaque AI technologies.
By virtue of its interdisciplinary nature and highly specialized content, the edited volume will likely (and primarily) be addressed to expert audiences, namely, academics, regulators and practitioners in a variety of fields (lawyers and engineers). To the extent that it will include contributions realized in a variety of fields, it is likely to attract significant readership in the social sciences (particularly, law and political science), and data science and engineering.