Interdisciplinary Expertise to Advance Equitable Explainable AI
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916303419211776 |
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| author | Bennett, Chloe R. Cole-Lewis, Heather Farquhar, Stephanie Haamel, Naama Babenko, Boris Lang, Oran Fleck, Mat Traynis, Ilana Lau, Charles Horn, Ivor Lyles, Courtney |
| author_facet | Bennett, Chloe R. Cole-Lewis, Heather Farquhar, Stephanie Haamel, Naama Babenko, Boris Lang, Oran Fleck, Mat Traynis, Ilana Lau, Charles Horn, Ivor Lyles, Courtney |
| contents | The field of artificial intelligence (AI) is rapidly influencing health and healthcare, but bias and poor performance persists for populations who face widespread structural oppression. Previous work has clearly outlined the need for more rigorous attention to data representativeness and model performance to advance equity and reduce bias. However, there is an opportunity to also improve the explainability of AI by leveraging best practices of social epidemiology and health equity to help us develop hypotheses for associations found. In this paper, we focus on explainable AI (XAI) and describe a framework for interdisciplinary expert panel review to discuss and critically assess AI model explanations from multiple perspectives and identify areas of bias and directions for future research. We emphasize the importance of the interdisciplinary expert panel to produce more accurate, equitable interpretations which are historically and contextually informed. Interdisciplinary panel discussions can help reduce bias, identify potential confounders, and identify opportunities for additional research where there are gaps in the literature. In turn, these insights can suggest opportunities for AI model improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18563 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Interdisciplinary Expertise to Advance Equitable Explainable AI Bennett, Chloe R. Cole-Lewis, Heather Farquhar, Stephanie Haamel, Naama Babenko, Boris Lang, Oran Fleck, Mat Traynis, Ilana Lau, Charles Horn, Ivor Lyles, Courtney Computers and Society Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning The field of artificial intelligence (AI) is rapidly influencing health and healthcare, but bias and poor performance persists for populations who face widespread structural oppression. Previous work has clearly outlined the need for more rigorous attention to data representativeness and model performance to advance equity and reduce bias. However, there is an opportunity to also improve the explainability of AI by leveraging best practices of social epidemiology and health equity to help us develop hypotheses for associations found. In this paper, we focus on explainable AI (XAI) and describe a framework for interdisciplinary expert panel review to discuss and critically assess AI model explanations from multiple perspectives and identify areas of bias and directions for future research. We emphasize the importance of the interdisciplinary expert panel to produce more accurate, equitable interpretations which are historically and contextually informed. Interdisciplinary panel discussions can help reduce bias, identify potential confounders, and identify opportunities for additional research where there are gaps in the literature. In turn, these insights can suggest opportunities for AI model improvement. |
| title | Interdisciplinary Expertise to Advance Equitable Explainable AI |
| topic | Computers and Society Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2406.18563 |