The dual function of explanations: why it is useful to compute explanations

Tsakalakis, Nikolaos, Stalla-Bourdillon, Sophie, Carmichael, Laura, Huynh, Trung Dong, Moreau, Luc and Helal, Ayah (2020) The dual function of explanations: why it is useful to compute explanations. Computers, Privacy & Data Protection 2021: Enforcing Rights in a Changing World, , Brussels, Belgium. 27 - 29 Jan 2021. (In Press)

Abstract

The increasing dependence of decision-making on some level of automation has naturally led to discussions about the trustworthiness of such automation, calls for transparent automated decision-making and the emergence of ‘explainable Artificial Intelligence’ (XAI). Although XAI research has produced a number of taxonomies for the explanation of Artificial Intelligence (AI) and Machine Learning (ML) models, the legal debate has so far been mainly focused on whether a ‘right to explanation’ exists in the GDPR. Lately, a growing body of interdisciplinary literature is concentrating on the goals and substance of explanations produced for automated decision-making, with a view to clarify their role and improve their value against unfairness, discrimination and opacity for the purposes ensuring compliance with Article 22 of the GDPR. At the same time, several researchers have warned that transparency of the algorithmic processes in itself is not enough and tools for better and easier assessment and review of the whole socio-technical system that include automated decision-making are needed. We suggest that explanations can be relevant for most of the obligations set forth by the GDPR and can assist towards a holistic compliance strategy if used as detective controls. Automating the creation of computable explanations that can support breach detection has the power to make compliance more systematic and to facilitate monitoring and auditing. Carefully constructed explanations can empower both data subjects and controllers and should be seen as key controls in order to meet accountability and data protection-by-design obligations. This paper thus presents the work undertaken by the PLEAD project towards ‘explainable-by-design’ socio-technical systems. PLEAD acknowledges the dual function of explanations as external detective controls (to benefit data subjects) and as internal detective controls (to benefit data controllers) and leverages provenance-based technology to compute explanations and support the deployment of systematic compliance strategies.

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Contributors

Author: Nikolaos Tsakalakis

Author: Sophie Stalla-Bourdillon

Author: Laura Carmichael

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