Prior Work

Before setting up the EXPLAIN project, the project partners already work towards the vision of  AI system which smartly interact with domain users. Here we collected the relevant prior work for the interested reader.

XAI and Explanatory ML

Baskan, D. and Erdelt, P.K., 2022. Neighborhood-Based Loss Functions for Explainability of Autoencoders. Available at SSRN 4212995.

Kotriwala A, Klöpper B, Dix M, Gopalakrishnan G, Ziobro D, Potschka A. XAI for Operations in the Process Industry-Applications, Theses, and Research Directions. In AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering 2021.

Schramowski, P., Stammer, W., Teso, S., Brugger, A., Herbert, F., Shao, X., Luigs, H.G., Mahlein, A.K. and Kersting, K., 2020. Making Deep Neural Networks Right for the Right Scientific Reasons by Interacting with Their Explanations. Nature Machine Intelligence, 2(8), pp.476-486.

Teso S, Kersting K. Explanatory Interactive Machine Learning. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society 2019 Jan 27 (pp. 239-245).

Human and Automation Interaction

Westin, C., Borst, C. and Hilburn, B., 2015. Strategic Conformance: Overcoming Acceptance Issues of Decision Aiding Automation?. IEEE Transactions on Human-Machine Systems, 46(1), pp.41-52.

Westin, C., Hilburn, B., Borst, C., Van Kampen, E.J. and Bång, M., 2020, October. Building Transparent and Personalized AI Support in Air Traffic Control. In 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC) (pp. 1-8). IEEE.

Industrial AI and Applications

W.D van Driel, P. Watté, interPack paper, Reliability of Electronic Drivers: An Industrial Approach, 2021.

Gamer T, Hoernicke M, Kloepper B, Bauer R, Isaksson AJ. The autonomous industrial plant–future of process engineering, operations and maintenance. Journal of Process Control. 2020 Apr 1;88:101-10.


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