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Interview: AI Expert Prof. Müller on XAI

Or How Far do We have to Go in Order to Get There?

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Notes

  1. https://research.com/scientists-rankings/computer-science/de last visited: 26.11.2021.

  2. https://scholar.google.com/citations?user=jplQac8AAAAJ&hl=en&oi=ao last visited: 27.11.2021.

  3. LRP: Layer-wise Relevance Propagation an introduction can be found in [2, 4].

  4. See https://www.itu.int/en/ITU-T/focusgroups/ml5g/Pages/default.aspx.

  5. See https://www.itu.int/en/ITU-T/focusgroups/ai4h/Pages/default.aspx.

References

  1. Samek W, Montavon G, Vedaldi A, Hansen LK, Müller KR (2019) Explainable AI: interpreting, explaining and visualizing deep learning, vol 11700. Springer Nature

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  2. Samek W, Montavon G, Lapuschkin S, Anders CJ, Müller KR (2021) Explaining deep neural networks and beyond: a review of methods and applications. Proc IEEE 109(3):247–278

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  3. Lapuschkin S, Wäldchen S, Binder A, Montavon G, Samek W, Müller KR (2019) Unmasking clever hans predictors and assessing what machines really learn. Nat Commun 10(1):1–8

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  4. Bach S, Binder A, Montavon G, Klauschen F, Müller KR, Samek W (2015) On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS One 10(7):e0130140

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  5. Binder A, Bockmayr M, Hägele M, Wienert S, Heim D, Hellweg K, Ishii M, Stenzinger A, Hocke A, Denkert C et al (2021) Morphological and molecular breast cancer profiling through explainable machine learning. Nat Mach Intell 3:355–366

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  6. Schütt KT, Arbabzadah F, Chmiela S, Müller KR, Tkatchenko A (2017) Quantum-chemical insights from deep tensor neural networks. Nat Commun 8(1):1–8

    Article  Google Scholar 

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Correspondence to Johannes Fähndrich.

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Fähndrich, J., Povalej, R., Rittelmeier, H. et al. Interview: AI Expert Prof. Müller on XAI. Künstl Intell 36, 181–184 (2022). https://doi.org/10.1007/s13218-022-00776-4

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