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

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…
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Taxonomy
TopicsEthics and Social Impacts of AI · Adversarial Robustness in Machine Learning · Safety Systems Engineering in Autonomy
