Towards a Novel Measure of User Trust in XAI Systems
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909675024285696 |
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| author | Miró-Nicolau, Miquel Moyà-Alcover, Gabriel Jaume-i-Capó, Antoni González-Hidalgo, Manuel Ghazel, Adel Campello, Maria Gemma Sempere Sancho, Juan Antonio Palmer |
| author_facet | Miró-Nicolau, Miquel Moyà-Alcover, Gabriel Jaume-i-Capó, Antoni González-Hidalgo, Manuel Ghazel, Adel Campello, Maria Gemma Sempere Sancho, Juan Antonio Palmer |
| contents | The increasing reliance on Deep Learning models, combined with their inherent lack of transparency, has spurred the development of a novel field of study known as eXplainable AI (XAI) methods. These methods seek to enhance the trust of end-users in automated systems by providing insights into the rationale behind their decisions. This paper presents a novel trust measure in XAI systems, allowing their refinement. Our proposed metric combines both performance metrics and trust indicators from an objective perspective. To validate this novel methodology, we conducted three case studies showing an improvement respect the state-of-the-art, with an increased sensitiviy to different scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05766 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards a Novel Measure of User Trust in XAI Systems Miró-Nicolau, Miquel Moyà-Alcover, Gabriel Jaume-i-Capó, Antoni González-Hidalgo, Manuel Ghazel, Adel Campello, Maria Gemma Sempere Sancho, Juan Antonio Palmer Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning The increasing reliance on Deep Learning models, combined with their inherent lack of transparency, has spurred the development of a novel field of study known as eXplainable AI (XAI) methods. These methods seek to enhance the trust of end-users in automated systems by providing insights into the rationale behind their decisions. This paper presents a novel trust measure in XAI systems, allowing their refinement. Our proposed metric combines both performance metrics and trust indicators from an objective perspective. To validate this novel methodology, we conducted three case studies showing an improvement respect the state-of-the-art, with an increased sensitiviy to different scenarios. |
| title | Towards a Novel Measure of User Trust in XAI Systems |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2405.05766 |