Müller, S., Toborek, V., Beckh, K., Jakobs, M., Bauckhage, C., Welke, P. (2023). An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science, vol 14171. Springer, Cham.
K. Beckh, S. Müller, M. Jakobs, V. Toborek, H. Tan, R. Fischer, P. Welke, S. Houben, L. von Rueden. "Harnessing Prior Knowledge for Explainable Machine Learning: An Overview," 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), Raleigh, NC, USA, 2023, pp. 450-463
K. Beckh, S. Müller, S. Rüping. "A Quantitative Human-Grounded Evaluation Process for Explainable Machine Learning." LWDA. 2022.
L. Von Rueden, S. Mayer, K. Beckh, B. Georgiev, S. Giesselbach, R. Heese, B. Kirsch, J. Pfrommer, A. Pick, R. Ramamurthy, M. Walczak, J. Garcke, C. Bauckhage, and J. Schuecker, “Informed Machine Learning – A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 1, pp. 614-633
Poster at TrustML @UBC, Vancouver, Canada
Talk at AAAI XAI4Sci workshop, Vancouver, Canada