Ethical Assurance: A practical approach to the responsible design, development, and deployment of data-driven technologies

TL;DR

This paper introduces 'ethical assurance', a structured, participatory framework for integrating ethical, social, and legal considerations into the development and deployment of data-driven AI systems, addressing current gaps in accountability and transparency.

Contribution

It generalizes argument-based assurance to create ethical assurance, unifying mechanisms for responsible AI through inclusive governance and practical evaluation methods.

Findings

Critical analysis of current algorithmic assessment efforts

Introduction of the ethical assurance framework

Identification of challenges and future research directions

Abstract

This article offers several contributions to the interdisciplinary project of responsible research and innovation in data science and AI. First, it provides a critical analysis of current efforts to establish practical mechanisms for algorithmic assessment, which are used to operationalise normative principles, such as sustainability, accountability, transparency, fairness, and explainability, in order to identify limitations and gaps with the current approaches. Second, it provides an accessible introduction to the methodology of argument-based assurance, and explores how it is currently being applied in the development of safety cases for autonomous and intelligent systems. Third, it generalises this method to incorporate wider ethical, social, and legal considerations, in turn establishing a novel version of argument-based assurance that we call 'ethical assurance'. Ethical assurance is presented as a structured means for unifying the myriad practical mechanisms that have been proposed, as it is built upon a process-based form of project governance that supports inclusive and participatory ethical deliberation while also remaining grounded in social and technical realities. Finally, it sets an agenda for ethical assurance, by detailing current challenges, open questions, and next steps, which serve as a springboard to build an active (and interdisciplinary) research programme as well as contribute to ongoing discussions in policy and governance.