Development of a persuasive User Experience Research (UXR) Point of View for Explainable Artificial Intelligence (XAI)
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912440318427136 |
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| author | Naiseh, Mohammad Dogan, Huseyin Giff, Stephen Jiang, Nan |
| author_facet | Naiseh, Mohammad Dogan, Huseyin Giff, Stephen Jiang, Nan |
| contents | Explainable Artificial Intelligence (XAI) plays a critical role in fostering user trust and understanding in AI-driven systems. However, the design of effective XAI interfaces presents significant challenges, particularly for UX professionals who may lack technical expertise in AI or machine learning. Existing explanation methods, such as SHAP, LIME, and counterfactual explanations, often rely on complex technical language and assumptions that are difficult for non-expert users to interpret. To address these gaps, we propose a UX Research (UXR) Playbook for XAI - a practical framework aimed at supporting UX professionals in designing accessible, transparent, and trustworthy AI experiences. Our playbook offers actionable guidance to help bridge the gap between technical explainability methods and user centred design, empowering designers to create AI interactions that foster better understanding, trust, and responsible AI adoption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16199 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Development of a persuasive User Experience Research (UXR) Point of View for Explainable Artificial Intelligence (XAI) Naiseh, Mohammad Dogan, Huseyin Giff, Stephen Jiang, Nan Human-Computer Interaction Explainable Artificial Intelligence (XAI) plays a critical role in fostering user trust and understanding in AI-driven systems. However, the design of effective XAI interfaces presents significant challenges, particularly for UX professionals who may lack technical expertise in AI or machine learning. Existing explanation methods, such as SHAP, LIME, and counterfactual explanations, often rely on complex technical language and assumptions that are difficult for non-expert users to interpret. To address these gaps, we propose a UX Research (UXR) Playbook for XAI - a practical framework aimed at supporting UX professionals in designing accessible, transparent, and trustworthy AI experiences. Our playbook offers actionable guidance to help bridge the gap between technical explainability methods and user centred design, empowering designers to create AI interactions that foster better understanding, trust, and responsible AI adoption. |
| title | Development of a persuasive User Experience Research (UXR) Point of View for Explainable Artificial Intelligence (XAI) |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2506.16199 |