Towards a Taxonomy for Explainable AI in Computational Pathology

Müller, Heimo; Kargl, Michaela; Plass, Markus; Kipperer, Bettina; Brčić, Luka; Regitnig, Peter; Geißler, Christian; Küster, Tobias; Zerbe, Norman; Holzinger, Andreas

doi:10.1007/978-3-030-72188-6_15

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Indeed, there are already some works that go into this direction. For instance, Müller et al [55] construct a taxonomy solely for the domain of computational pathology, and Tjoa and Guan [70] construct one for the medical domain.…”

Section: Taxonomies and Reviews Focusing On Specific Aspectsmentioning

confidence: 99%

A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

Speith

2022

2022 ACM Conference on Fairness, Accountability, and Transparency

167

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The recent surge in publications related to explainable artificial intelligence (XAI) has led to an almost insurmountable wall if one wants to get started or stay up to date with XAI. For this reason, articles and reviews that present taxonomies of XAI methods seem to be a welcomed way to get an overview of the field. Building on this idea, there is currently a trend of producing such taxonomies, leading to several competing approaches to construct them. In this paper, we will review recent approaches to constructing taxonomies of XAI methods and discuss general challenges concerning them as well as their individual advantages and limitations. Our review is intended to help scholars be aware of challenges current taxonomies face. As we will argue, when charting the field of XAI, it may not be sufficient to rely on one of the approaches we found. To amend this problem, we will propose and discuss three possible solutions: a new taxonomy that incorporates the reviewed ones, a database of XAI methods, and a decision tree to help choose fitting methods. CCS CONCEPTS• General and reference → Surveys and overviews; • Computing methodologies → Artificial intelligence.

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Section: Taxonomies and Reviews Focusing On Specific Aspectsmentioning

confidence: 99%

A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

Speith

2022

2022 ACM Conference on Fairness, Accountability, and Transparency

167

View full text Add to dashboard Cite

show abstract

“…Besides communication to the general public, we also disseminated project results to the scientific community in articles providing a project overview, 13 describing aspects implemented in the EMPAIA platform, 18 , 36 , 48 giving recommendations to different stakeholders, 14 , 17 , 22 , 49 and presenting other EMPAIA-related research. 39 , 40 , 41 , 42 , 50 , 51 , 52 , 53 Furthermore, congresses and events worldwide presented opportunities to showcase EMPAIA. These events allowed talking to media representatives, pathologists, and AI experts and to gain visibility and exchange information about the project and current developments in the field of digital pathology to establish and intensify networks.…”

Section: Communication and Knowledge Transfermentioning

confidence: 99%