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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.04638 |
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| _version_ | 1866916197264523264 |
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| author | Rubegni, Elisa Ayoub, Omran Rizzo, Stefania Maria Rita Barbero, Marco Bernegger, Guenda Faraci, Francesca Mangili, Francesca Soldini, Emiliano Trimboli, Pierpaolo Facchini, Alessandro |
| author_facet | Rubegni, Elisa Ayoub, Omran Rizzo, Stefania Maria Rita Barbero, Marco Bernegger, Guenda Faraci, Francesca Mangili, Francesca Soldini, Emiliano Trimboli, Pierpaolo Facchini, Alessandro |
| contents | The widespread use of Artificial Intelligence-based tools in the healthcare sector raises many ethical and legal problems, one of the main reasons being their black-box nature and therefore the seemingly opacity and inscrutability of their characteristics and decision-making process. Literature extensively discusses how this can lead to phenomena of over-reliance and under-reliance, ultimately limiting the adoption of AI. We addressed these issues by building a theoretical framework based on three concepts: Feature Importance, Counterexample Explanations, and Similar-Case Explanations. Grounded in the literature, the model was deployed within a case study in which, using a participatory design approach, we designed and developed a high-fidelity prototype. Through the co-design and development of the prototype and the underlying model, we advanced the knowledge on how to design AI-based systems for enabling complementarity in the decision-making process in the healthcare domain. Our work aims at contributing to the current discourse on designing AI systems to support clinicians' decision-making processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_04638 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Designing for Complementarity: A Conceptual Framework to Go Beyond the Current Paradigm of Using XAI in Healthcare Rubegni, Elisa Ayoub, Omran Rizzo, Stefania Maria Rita Barbero, Marco Bernegger, Guenda Faraci, Francesca Mangili, Francesca Soldini, Emiliano Trimboli, Pierpaolo Facchini, Alessandro Human-Computer Interaction The widespread use of Artificial Intelligence-based tools in the healthcare sector raises many ethical and legal problems, one of the main reasons being their black-box nature and therefore the seemingly opacity and inscrutability of their characteristics and decision-making process. Literature extensively discusses how this can lead to phenomena of over-reliance and under-reliance, ultimately limiting the adoption of AI. We addressed these issues by building a theoretical framework based on three concepts: Feature Importance, Counterexample Explanations, and Similar-Case Explanations. Grounded in the literature, the model was deployed within a case study in which, using a participatory design approach, we designed and developed a high-fidelity prototype. Through the co-design and development of the prototype and the underlying model, we advanced the knowledge on how to design AI-based systems for enabling complementarity in the decision-making process in the healthcare domain. Our work aims at contributing to the current discourse on designing AI systems to support clinicians' decision-making processes. |
| title | Designing for Complementarity: A Conceptual Framework to Go Beyond the Current Paradigm of Using XAI in Healthcare |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2404.04638 |