Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
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Retzlaff, CO; Angerschmid, A; Saranti, A; Schneeberger, D; Röttger, R; Müller, H; Holzinger, A.
Post-hoc vs ante-hoc explanations: xAI design guidelines for data scientists
COGN SYST RES. 2024; 86: 101243
Doi: 10.1016/j.cogsys.2024.101243
Web of Science
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- Führende Autor*innen der Med Uni Graz
- Holzinger Andreas
- Co-Autor*innen der Med Uni Graz
- Angerschmid Alessa
- Müller Heimo
- Saranti Anna
- Schneeberger David Michael
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- Abstract:
- The growing field of explainable Artificial Intelligence (xAI) has given rise to a multitude of techniques and methodologies, yet this expansion has created a growing gap between existing xAI approaches and their practical application. This poses a considerable obstacle for data scientists striving to identify the optimal xAI technique for their needs. To address this problem, our study presents a customized decision support framework to aid data scientists in choosing a suitable xAI approach for their use -case. Drawing from a literature survey and insights from interviews with five experienced data scientists, we introduce a decision tree based on the trade-offs inherent in various xAI approaches, guiding the selection between six commonly used xAI tools. Our work critically examines six prevalent ante -hoc and post -hoc xAI methods, assessing their applicability in real -world contexts through expert interviews. The aim is to equip data scientists and policymakers with the capacity to select xAI methods that not only demystify the decision -making process, but also enrich user understanding and interpretation, ultimately advancing the application of xAI in practical settings.
- Find related publications in this database (Keywords)
- Explainable AI
- xAI
- Post-hoc
- Ante-hoc
- Explanations
- Guideline