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Addressing regulatory requirements on explanations for automated decisions with provenance – a case study

Addressing regulatory requirements on explanations for automated decisions with provenance – a case study
Addressing regulatory requirements on explanations for automated decisions with provenance – a case study
AI-based automated decisions are increasingly used as part of new services being deployed to the general public. This approach to building services presents significant potential benefits, such as the reduced speed of execution, increased accuracy, lower cost, and ability to adapt to a wide variety of situations. However, equally significant concerns have been raised and are now well documented such as concerns about privacy, fairness, bias and ethics. On the consumer side, more often than not, the users of those services are provided with no or inadequate explanations for decisions that may impact their lives. In this paper, we report the experience of developing a socio-technical approach to constructing explanations for such decisions from their audit trails, or provenance, in an automated manner. The work has been carried out in collaboration with the UK Information Commissioner’s Office (ICO). In particular, we have implemented an automated Loan Decision scenario, instrumented its decision pipeline to record provenance, categorized relevant explanations according to their audience and their regulatory purposes, built an explanation-generation prototype, and deployed the whole system in an online demonstrator.
Huynh, Trung Dong
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Tsakalakis, Nikolaos
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Helal, Ayah
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Stalla-Bourdillon, Sophie
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Moreau, Luc
0b53974f-3e78-4c56-a47e-799d9f220911
Huynh, Trung Dong
9e04643c-cdd0-41ce-a641-00df4804280f
Tsakalakis, Nikolaos
eae42e98-58b8-45b9-8c11-35a798cc9671
Helal, Ayah
0e44c81d-ef58-4503-b8cb-3d1fb82ec651
Stalla-Bourdillon, Sophie
c189651b-9ed3-49f6-bf37-25a47c487164
Moreau, Luc
0b53974f-3e78-4c56-a47e-799d9f220911

Huynh, Trung Dong, Tsakalakis, Nikolaos, Helal, Ayah, Stalla-Bourdillon, Sophie and Moreau, Luc (2021) Addressing regulatory requirements on explanations for automated decisions with provenance – a case study. Digital Government: Research and Practice, [16e]. (doi:10.1145/3436897).

Record type: Article

Abstract

AI-based automated decisions are increasingly used as part of new services being deployed to the general public. This approach to building services presents significant potential benefits, such as the reduced speed of execution, increased accuracy, lower cost, and ability to adapt to a wide variety of situations. However, equally significant concerns have been raised and are now well documented such as concerns about privacy, fairness, bias and ethics. On the consumer side, more often than not, the users of those services are provided with no or inadequate explanations for decisions that may impact their lives. In this paper, we report the experience of developing a socio-technical approach to constructing explanations for such decisions from their audit trails, or provenance, in an automated manner. The work has been carried out in collaboration with the UK Information Commissioner’s Office (ICO). In particular, we have implemented an automated Loan Decision scenario, instrumented its decision pipeline to record provenance, categorized relevant explanations according to their audience and their regulatory purposes, built an explanation-generation prototype, and deployed the whole system in an online demonstrator.

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dgov-ico-case-study-v3 - Accepted Manuscript
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Accepted/In Press date: 15 November 2020
e-pub ahead of print date: 20 January 2021
Published date: 1 March 2021

Identifiers

Local EPrints ID: 446156
URI: http://eprints.soton.ac.uk/id/eprint/446156
PURE UUID: 40323f3b-dae0-4283-a69d-f12c2f09fd3e
ORCID for Nikolaos Tsakalakis: ORCID iD orcid.org/0000-0003-2654-0825
ORCID for Sophie Stalla-Bourdillon: ORCID iD orcid.org/0000-0003-3715-1219

Catalogue record

Date deposited: 22 Jan 2021 17:31
Last modified: 08 Jul 2022 01:46

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Contributors

Author: Trung Dong Huynh
Author: Ayah Helal
Author: Luc Moreau

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