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A methodology and software architecture to support explainability-by-design

A methodology and software architecture to support explainability-by-design
A methodology and software architecture to support explainability-by-design
Algorithms play a crucial role in many technological systems that control or affect various aspects of our lives. As a result, providing explanations for their decisions to address the needs of users and organisations is increasingly expected by laws, regulations, codes of conduct, and the public. However, as laws and regulations do not prescribe how to meet such expectations, organisations are often left to devise their own approaches to explainability, inevitably increasing the cost of compliance and good governance. Hence, we envision Explainability-by-Design, a holistic methodology characterised by proactive measures to include explanation capability in the design of decision-making systems. The methodology consists of three phases: (A) Explanation Requirement Analysis, (B) Explanation Technical Design, and (C) Explanation Validation. This paper describes phase (B), a technical workflow to implement explanation capability from requirements elicited by domain experts for a specific application context. Outputs of this phase are a set of configurations, allowing a reusable explanation service to exploit logs provided by the target application to create provenance traces of the application's decisions. The provenance then can be queried to extract relevant data points, which can be used in explanation plans to construct explanations personalised to their consumers. Following the workflow, organisations can design their decision-making systems to produce explanations that meet the specified requirements. To facilitate the process, we present a software architecture with reusable components to incorporate the resulting explanation capability into an application. Finally, we applied the workflow to two application scenarios and measured the associated development costs. It was shown that the approach is tractable in terms of development time, which can be as low as two hours per sentence.
arXiv
Huynh, T.D.
ddea6cf3-5a82-4c99-8883-7c31cf22dd36
Tsakalakis, N.
7d9c7129-d04e-4ed6-aefa-12371a007b95
Helal, A.
0e44c81d-ef58-4503-b8cb-3d1fb82ec651
Stalla-Bourdillon, S.
c189651b-9ed3-49f6-bf37-25a47c487164
Moreau, L.
033c63dd-3fe9-4040-849f-dfccbe0406f8
Huynh, T.D.
ddea6cf3-5a82-4c99-8883-7c31cf22dd36
Tsakalakis, N.
7d9c7129-d04e-4ed6-aefa-12371a007b95
Helal, A.
0e44c81d-ef58-4503-b8cb-3d1fb82ec651
Stalla-Bourdillon, S.
c189651b-9ed3-49f6-bf37-25a47c487164
Moreau, L.
033c63dd-3fe9-4040-849f-dfccbe0406f8

Huynh, T.D., Tsakalakis, N., Helal, A., Stalla-Bourdillon, S. and Moreau, L. (2022) A methodology and software architecture to support explainability-by-design arXiv 14pp. (doi:10.48550/arxiv.2206.06251).

Record type: Monograph (Discussion Paper)

Abstract

Algorithms play a crucial role in many technological systems that control or affect various aspects of our lives. As a result, providing explanations for their decisions to address the needs of users and organisations is increasingly expected by laws, regulations, codes of conduct, and the public. However, as laws and regulations do not prescribe how to meet such expectations, organisations are often left to devise their own approaches to explainability, inevitably increasing the cost of compliance and good governance. Hence, we envision Explainability-by-Design, a holistic methodology characterised by proactive measures to include explanation capability in the design of decision-making systems. The methodology consists of three phases: (A) Explanation Requirement Analysis, (B) Explanation Technical Design, and (C) Explanation Validation. This paper describes phase (B), a technical workflow to implement explanation capability from requirements elicited by domain experts for a specific application context. Outputs of this phase are a set of configurations, allowing a reusable explanation service to exploit logs provided by the target application to create provenance traces of the application's decisions. The provenance then can be queried to extract relevant data points, which can be used in explanation plans to construct explanations personalised to their consumers. Following the workflow, organisations can design their decision-making systems to produce explanations that meet the specified requirements. To facilitate the process, we present a software architecture with reusable components to incorporate the resulting explanation capability into an application. Finally, we applied the workflow to two application scenarios and measured the associated development costs. It was shown that the approach is tractable in terms of development time, which can be as low as two hours per sentence.

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Published date: 2022

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Local EPrints ID: 504736
URI: http://eprints.soton.ac.uk/id/eprint/504736
PURE UUID: 57d73cf1-84d8-4d51-847b-3cc0a0248161
ORCID for T.D. Huynh: ORCID iD orcid.org/0000-0003-4937-2473
ORCID for S. Stalla-Bourdillon: ORCID iD orcid.org/0000-0003-3715-1219
ORCID for L. Moreau: ORCID iD orcid.org/0000-0002-3494-120X

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Date deposited: 18 Sep 2025 16:54
Last modified: 20 Sep 2025 01:51

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Contributors

Author: T.D. Huynh ORCID iD
Author: N. Tsakalakis
Author: A. Helal
Author: L. Moreau ORCID iD

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