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

TY - JOUR

T1 - The dual function of explanations: Why it is useful to compute explanations

AU - Tsakalakis, Niko

AU - Stalla-Bourdillon, Sophie

AU - Carmichael, Laura

AU - Huynh, Trung Dong

AU - Moreau, Luc

AU - Helal, Ayah

N1 - Funding Information: The work presented here has been supported by the UK Engineering and Physical Sciences Research Council ( EPSRC ) under Grant numbers EP/S027238/1 and EP/S027254/1 . Publisher Copyright: © 2021 Niko Tsakalakis, Sophie Stalla-Bourdillon, Laura Carmichael, Trung Dong Huynh, Luc Moreau, Ayah Helal Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/7

Y1 - 2021/7

N2 - Whilst the legal debate concerning automated decision-making has been focused mainly on whether a ‘right to explanation’ exists in the GDPR, the emergence of ‘explainable Artificial Intelligence’ (XAI) has produced taxonomies for the explanation of Artificial Intelligence (AI) systems. However, various researchers have warned that transparency of the algorithmic processes in itself is not enough. Better and easier tools for the assessment and review of the socio-technical systems that incorporate automated decision-making are needed. The PLEAD project suggests that, aside from fulfilling the obligations set forth by Article 22 of the GDPR, explanations can also assist towards a holistic compliance strategy if used as detective controls. PLEAD aims to show that computable explanations can facilitate monitoring and auditing, and make compliance more systematic. Automated computable explanations can be key controls in fulfilling accountability and data-protection-by-design obligations, able to empower both controllers and data subjects. This opinion piece presents the work undertaken by the PLEAD project towards facilitating the generation of computable explanations. PLEAD leverages provenance-based technology to compute explanations as external detective controls to the benefit of data subjects and as internal detective controls to the benefit of the data controller.

AB - Whilst the legal debate concerning automated decision-making has been focused mainly on whether a ‘right to explanation’ exists in the GDPR, the emergence of ‘explainable Artificial Intelligence’ (XAI) has produced taxonomies for the explanation of Artificial Intelligence (AI) systems. However, various researchers have warned that transparency of the algorithmic processes in itself is not enough. Better and easier tools for the assessment and review of the socio-technical systems that incorporate automated decision-making are needed. The PLEAD project suggests that, aside from fulfilling the obligations set forth by Article 22 of the GDPR, explanations can also assist towards a holistic compliance strategy if used as detective controls. PLEAD aims to show that computable explanations can facilitate monitoring and auditing, and make compliance more systematic. Automated computable explanations can be key controls in fulfilling accountability and data-protection-by-design obligations, able to empower both controllers and data subjects. This opinion piece presents the work undertaken by the PLEAD project towards facilitating the generation of computable explanations. PLEAD leverages provenance-based technology to compute explanations as external detective controls to the benefit of data subjects and as internal detective controls to the benefit of the data controller.

UR - http://www.scopus.com/inward/record.url?scp=85102654791&partnerID=8YFLogxK

U2 - 10.1016/j.clsr.2020.105527

DO - 10.1016/j.clsr.2020.105527

M3 - Article

SN - 0267-3649

VL - 41

JO - Computer Law & Security Review

JF - Computer Law & Security Review

M1 - 105527

ER -