Integrating Evidence into the Design of XAI and AI-based Decision Support Systems: A Means-End Framework for End-users in Construction
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914024884535296 |
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| author | Love, Peter E. D. Matthews, Jane Fang, Weili Mahamivanan, Hadi |
| author_facet | Love, Peter E. D. Matthews, Jane Fang, Weili Mahamivanan, Hadi |
| contents | Explainable Artificial Intelligence seeks to make the reasoning processes of AI models transparent and interpretable, particularly in complex decision making environments. In the construction industry, where AI based decision support systems are increasingly adopted, limited attention has been paid to the integration of supporting evidence that underpins the reliability and accountability of AI generated outputs. The absence of such evidence undermines the validity of explanations and the trustworthiness of system recommendations. This paper addresses this gap by introducing a theoretical, evidence based means end framework developed through a narrative review. The framework offers an epistemic foundation for designing XAI enabled DSS that generate meaningful explanations tailored to users knowledge needs and decision contexts. It focuses on evaluating the strength, relevance, and utility of different types of evidence supporting AI generated explanations. While developed with construction professionals as primary end users, the framework is also applicable to developers, regulators, and project managers with varying epistemic goals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14209 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Integrating Evidence into the Design of XAI and AI-based Decision Support Systems: A Means-End Framework for End-users in Construction Love, Peter E. D. Matthews, Jane Fang, Weili Mahamivanan, Hadi Human-Computer Interaction Artificial Intelligence Explainable Artificial Intelligence seeks to make the reasoning processes of AI models transparent and interpretable, particularly in complex decision making environments. In the construction industry, where AI based decision support systems are increasingly adopted, limited attention has been paid to the integration of supporting evidence that underpins the reliability and accountability of AI generated outputs. The absence of such evidence undermines the validity of explanations and the trustworthiness of system recommendations. This paper addresses this gap by introducing a theoretical, evidence based means end framework developed through a narrative review. The framework offers an epistemic foundation for designing XAI enabled DSS that generate meaningful explanations tailored to users knowledge needs and decision contexts. It focuses on evaluating the strength, relevance, and utility of different types of evidence supporting AI generated explanations. While developed with construction professionals as primary end users, the framework is also applicable to developers, regulators, and project managers with varying epistemic goals. |
| title | Integrating Evidence into the Design of XAI and AI-based Decision Support Systems: A Means-End Framework for End-users in Construction |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2412.14209 |