Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911845156126720 |
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| author | Chatzimparmpas, Angelos Kucher, Kostiantyn Kerren, Andreas |
| author_facet | Chatzimparmpas, Angelos Kucher, Kostiantyn Kerren, Andreas |
| contents | Visualization for explainable and trustworthy machine learning remains one of the most important and heavily researched fields within information visualization and visual analytics with various application domains, such as medicine, finance, and bioinformatics. After our 2020 state-of-the-art report comprising 200 techniques, we have persistently collected peer-reviewed articles describing visualization techniques, categorized them based on the previously established categorization schema consisting of 119 categories, and provided the resulting collection of 542 techniques in an online survey browser. In this survey article, we present the updated findings of new analyses of this dataset as of fall 2023 and discuss trends, insights, and eight open challenges for using visualizations in machine learning. Our results corroborate the rapidly growing trend of visualization techniques for increasing trust in machine learning models in the past three years, with visualization found to help improve popular model explainability methods and check new deep learning architectures, for instance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12005 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023 Chatzimparmpas, Angelos Kucher, Kostiantyn Kerren, Andreas Human-Computer Interaction Machine Learning Visualization for explainable and trustworthy machine learning remains one of the most important and heavily researched fields within information visualization and visual analytics with various application domains, such as medicine, finance, and bioinformatics. After our 2020 state-of-the-art report comprising 200 techniques, we have persistently collected peer-reviewed articles describing visualization techniques, categorized them based on the previously established categorization schema consisting of 119 categories, and provided the resulting collection of 542 techniques in an online survey browser. In this survey article, we present the updated findings of new analyses of this dataset as of fall 2023 and discuss trends, insights, and eight open challenges for using visualizations in machine learning. Our results corroborate the rapidly growing trend of visualization techniques for increasing trust in machine learning models in the past three years, with visualization found to help improve popular model explainability methods and check new deep learning architectures, for instance. |
| title | Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023 |
| topic | Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2403.12005 |