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This article offers several contributions to the interdisciplinary project ofresponsible research and innovation in data science and AI. First, it providesa critical analysis of current efforts to establish practical mechanisms foralgorithmic assessment, which are used to operationalise normative principles,such as sustainability, accountability, transparency, fairness, andexplainability, in order to identify limitations and gaps with the currentapproaches. Second, it provides an accessible introduction to the methodologyof argument-based assurance, and explores how it is currently being applied inthe development of safety cases for autonomous and intelligent systems. Third,it generalises this method to incorporate wider ethical, social, and legalconsiderations, in turn establishing a novel version of argument-basedassurance that we call 'ethical assurance'. Ethical assurance is presented as astructured means for unifying the myriad practical mechanisms that have beenproposed, as it is built upon a process-based form of project governance thatsupports inclusive and participatory ethical deliberation while also remaininggrounded in social and technical realities. Finally, it sets an agenda forethical assurance, by detailing current challenges, open questions, and nextsteps, which serve as a springboard to build an active (and interdisciplinary)research programme as well as contribute to ongoing discussions in policy andgovernance.

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