Towards User-Focused Research in Training Data Attribution for Human-Centered Explainable AI
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915587578396672 |
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| author | Nguyen, Elisa Bertram, Johannes Kortukov, Evgenii Song, Jean Y. Oh, Seong Joon |
| author_facet | Nguyen, Elisa Bertram, Johannes Kortukov, Evgenii Song, Jean Y. Oh, Seong Joon |
| contents | Explainable AI (XAI) aims to make AI systems more transparent, yet many practices emphasise mathematical rigour over practical user needs. We propose an alternative to this model-centric approach by following a design thinking process for the emerging XAI field of training data attribution (TDA), which risks repeating solutionist patterns seen in other subfields. However, because TDA is in its early stages, there is a valuable opportunity to shape its direction through user-centred practices. We engage directly with machine learning developers via a needfinding interview study (N=6) and a scenario-based interactive user study (N=31) to ground explanations in real workflows. Our exploration of the TDA design space reveals novel tasks for data-centric explanations useful to developers, such as grouping training samples behind specific model behaviours or identifying undersampled data. We invite the TDA, XAI, and HCI communities to engage with these tasks to strengthen their research's practical relevance and human impact. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16978 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards User-Focused Research in Training Data Attribution for Human-Centered Explainable AI Nguyen, Elisa Bertram, Johannes Kortukov, Evgenii Song, Jean Y. Oh, Seong Joon Human-Computer Interaction Artificial Intelligence Machine Learning Explainable AI (XAI) aims to make AI systems more transparent, yet many practices emphasise mathematical rigour over practical user needs. We propose an alternative to this model-centric approach by following a design thinking process for the emerging XAI field of training data attribution (TDA), which risks repeating solutionist patterns seen in other subfields. However, because TDA is in its early stages, there is a valuable opportunity to shape its direction through user-centred practices. We engage directly with machine learning developers via a needfinding interview study (N=6) and a scenario-based interactive user study (N=31) to ground explanations in real workflows. Our exploration of the TDA design space reveals novel tasks for data-centric explanations useful to developers, such as grouping training samples behind specific model behaviours or identifying undersampled data. We invite the TDA, XAI, and HCI communities to engage with these tasks to strengthen their research's practical relevance and human impact. |
| title | Towards User-Focused Research in Training Data Attribution for Human-Centered Explainable AI |
| topic | Human-Computer Interaction Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.16978 |