Preece, Alun David  ORCID: https://orcid.org/0000-0003-0349-9057, Ashelford, Rob, Armstrong, Harry and Braines, David
2018.
Hows and whys of artificial intelligence for public sector decisions: Explanation and evaluation.
Presented at: AAAI FSS-18: Artificial Intelligence in Government and Public Sector Proceedings,
Arlington, VA, USA,
18-20 October 2018.
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Abstract
Evaluation has always been a key challenge in the development of artificial intelligence (AI) based software, due to the technical complexity of the software artifact and, often, its embedding in complex sociotechnical processes. Recent advances in machine learning (ML) enabled by deep neural networks has exacerbated the challenge of evaluating such software due to the opaque nature of these ML-based artifacts. A key related issue is the (in)ability of such systems to generate useful explanations of their outputs, and we argue that the explanation and evaluation problems are closely linked. The paper models the elements of a ML-based AI system in the context of public sector decision (PSD) applications involving both artificial and human intelligence, and maps these elements against issues in both evaluation and explanation, showing how the two are related. We consider a number of common PSD application patterns in the light of our model, and identify a set of key issues connected to explanation and evaluation in each case. Finally, we propose multiple strategies to promote wider adoption of AI/ML technologies in PSD, where each is distinguished by a focus on different elements of our model, allowing PSD policy makers to adopt an approach that best fits their context and concerns.
| Item Type: | Conference or Workshop Item - unpublished |
|---|---|
| Date Type: | Publication |
| Status: | Unpublished |
| Schools: | Schools > Computer Science & Informatics Research Institutes & Centres > Crime and Security Research Institute (CSURI) |
| Related URLs: | |
| Date of First Compliant Deposit: | 18 October 2018 |
| Last Modified: | 15 Oct 2026 14:24 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/115993 |
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