Prior Work
Before setting up the EXPLAIN project, the project partners already worked towards the vision of AI systems that smartly interact with domain users. Here we collected the relevant prior work for the interested reader.
XAI and Explanatory ML
D. Baskan, and P. K. Erdelt,. Neighborhood-Based Loss Functions for Explainability of Autoencoders. Available at SSRN 4212995. 2022.
A. Kotriwala, B. Klöpper, M. Dix, G. Gopalakrishnan, D. Ziobro, A. Potschka. XAI for Operations in the Process Industry-Applications, Theses, and Research Directions. In AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering 2021.
P. Schramowski, W. Stammer, S. Teso, A. Brugger, F. Herbert,X. Shao, H. G. Luigs,A. K. Mahlein, and K. Kersting, Making Deep Neural Networks Right for the Right Scientific Reasons by Interacting with Their Explanations. Nature Machine Intelligence, 2(8), pp.476-486. 2020.
S. Teso and K. Kersting., 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
C. Westin, C. Borst, and B. Hilburn,. Strategic Conformance: Overcoming Acceptance Issues of Decision Aiding Automation?. IEEE Transactions on Human-Machine Systems, 46(1), pp.41-52. 2015.
C. Westin, B. Hilburn, C. Borst, E. J. Van Kampen, and M. Bång,. 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.
T. Gamer, M. Hoernicke, B. Klöpper, R. Bauer, A. J. Isaksson., The autonomous industrial plant–future of process engineering, operations and maintenance. Journal of Process Control. 2020 Apr 1;88:101-10.
Publications
In the EXPLAIN project, the partners work on research regarding AI systems and Machine Learning approaches. Here we collect the publications done during the project execution.
Industrial AI and Applications
D. E. Baskan, D. Meyer, S. Mieck, L. Faubel, B. Klöpper, N. Strem, J. A. Wagner, and J. J. Koltermann, A Scenario-Based Model Comparison for Short-Term Day- Ahead Electricity Prices in Times of Economic and Political Tension. Algorithms, vol.16, no. 4, p.177 , March. 2023, doi: 10.3390/a16040177.
MLOps in Industry
L. Faubel, K. Schmid and H. Eichelberger, Is MLOps different in Industry 4.0? General and Specific Challenges , 3rd International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL) pp. 161-167. SciTePress., 2022, 10.5220/0011589600003329.
L. Faubel and K. Schmid, An Analysis of MLOps Practices, 1/2023, SSE 1/23/E, Software Systems Engineering, Institut für Informatik, Universität Hildesheim., 2023.
L.Faubel and K. Schmid, Review Protocol: A systematic literature review of MLOps, SSE 2/23/E, Stiftung Universität Hildesheim, August 2023
L.Faubel, T. Woudsma, L. Methnani, A. G. Ghezeljhemeidan, F. Buelow, K. Schmid,… M. Bång., (2023). Towards an MLOps Architecture for XAI in Industrial Applications. arXiv, 2309.12756.
L. Faubel, K.Schmid, & H. Eichelberger. MLOps Challenges in Industry 4.0. SN Comput. Sci., 4(6), 1–11. Doi: 10.1007/s42979-023-02282-2.
L. Faubel and Schmid, K. An MLOps Platform Comparison. (2024). Doi: 10.25528/197
L. Faubel and Schmid, K. MLOps: A Multiple Case Study in Industry 4.0. (2024) ArXiv, 2407.09107.
Human and Automation Interaction
A. Ramesh, M. Englund, A. Theodorou, R. Stolkin, and M. Chiou, Robot Health Indicator: A visual Cue to Improve Level of Autonomy Switching Systems. In: Variable Autonomy for human – robot Teaming (VAT) workshop, co – located with ACM/IEEE HRI 2023.
G. Manca, N. Bhattacharya, S. Maczey, D. Ziobro, E. Brorsson, and M. Bång, XAIProcessLens: A Counterfactual-Based Dashboard for Explainable AI in Process Industries,in Frontiers in Artificial Intelligence and Applications, vol. 368, HHAI 2023: Augmenting Human Intellect, pp. 401-403, doi: 10.3233/FAIA230110.
AI Governance
K. Baum, J. Bryson, F. Dignum, V. Dignum, M. Grobelnik, H. Hoos, M. Irgens, P. Lukowicz, C. Muller, F. Rossi, J. Shawe-Taylor, A. Theodorou and R. Vinuesa, From fear to action: AI governance and opportunities for all. 2023
Explainable AI in Industry
G. Manca, A. Fay, Explainable AI for Industrial flood classification using Counterfactuals, 2023.
G. Manca, F. C. Kunze, E. Brorsson and A. Fay, Dynamic Causal Analysis with Operator-Centric Visualization for Managing Industrial Alarm Floods, in: 2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference (ONCON)., 2023