Explainable AI for Human-Centric Smart Grids: A Conceptual Framework for Trustworthy Renewable Energy Systems
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Abstract
From probabilistic solar forecasting and wind prediction, to real-time dispatch, fault detection and demand response orchestration, the use of artificial intelligence (AI) in the modern smart grid is an indispensable necessity. But these predictive benefits of deep and ensemble learning have come at the expense of interpretability, leading to a legitimacy gap in a publicly accountable and safety-critical infrastructure that requires operators to defend switching decisions, regulators to examine market performance and prosumers to agree to automated control of their assets. The current power systems explainable AI (XAI) literature is mostly technique-driven and assesses the quality of explanations using algorithmic fidelity, but not in relation to the impact of explanations on humans who have to act on them. In this paper, a conceptual framework is proposed called Human-Centric Explainable Grid (HX-Grid) that proposes a shift of explainability from being a post-hoc property of a model to being a sociotechnical property of the grid. Adopting a design science and theory-synthesis approach, the study brings together four streams of literature (AI in renewable-integrated grids, foundations of XAI, trust and human factors, and energy governance) and proposes an architecture that encompasses the physical context and modelling, the generation of the explanation, the adaptation to the perspective of stakeholders, and the feedback from governance. The framework is expanded using a matrix of stakeholders and an explanation, six propositions, and a protocol for assessing the constructs and instruments for later empirical testing, which will be based on placeholders. The contribution reflects a theoretical and design aspect, without any empirical data being reported. The implications for grid operators, technology vendors and regulators of new AI accountability frameworks are explored, as well as a research agenda on trust calibration in the energy sector
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References
Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
Ahmad, T., Zhu, H., Zhang, D., Tariq, R., Bassam, A., Ullah, F., AlGhamdi, A. S., & Alshamrani, S. S. (2022). Energetics systems and artificial intelligence: Applications of industry 4.0. Energy Reports, 8, 334–361. https://doi.org/10.1016/j.egyr.2021.11.256
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning (arXiv:1702.08608). arXiv. https://doi.org/10.48550/arXiv.1702.08608
European Commission. (2021). Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts (COM(2021) 206 final). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021PC0206
Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a “right to explanation”. AI Magazine, 38(3), 50–57. https://doi.org/10.1609/aimag.v38i3.2741
Gungor, V. C., Sahin, D., Kocak, T., Ergut, S., Buccella, C., Cecati, C., & Hancke, G. P. (2011). Smart grid technologies: Communication technologies and standards. IEEE Transactions on Industrial Informatics, 7(4), 529–539. https://doi.org/10.1109/TII.2011.2166794
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105. https://doi.org/10.2307/25148625
Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2018). Metrics for explainable AI: Challenges and prospects (arXiv:1812.04608). arXiv. https://doi.org/10.48550/arXiv.1812.04608
Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10(1–2), 18–26. https://doi.org/10.1007/s13162-020-00161-0
Jacovi, A., Marasović, A., Miller, T., & Goldberg, Y. (2021). Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in AI. In
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 624–635). Association for Computing Machinery. https://doi.org/10.1145/3442188.3445923
Kuzlu, M., Cali, U., Sharma, V., & Guler, O. (2020). Gaining insight into solar photovoltaic power generation forecasting utilizing explainable artificial intelligence tools. IEEE Access, 8, 187814–187823. https://doi.org/10.1109/ACCESS.2020.3031477
Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (pp. 4765–4774). Curran Associates.
Machlev, R., Heistrene, L., Perl, M., Levy, K. Y., Belikov, J., Mannor, S., & Levron, Y. (2022). Explainable artificial intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities. Energy and AI, 9, 100169. https://doi.org/10.1016/j.egyai.2022.100169
Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38. https://doi.org/10.1016/j.artint.2018.07.007
Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230–253. https://doi.org/10.1518/001872097778543886
Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77. https://doi.org/10.2753/MIS0742-1222240302
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939778
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision (pp. 618–626). IEEE. https://doi.org/10.1109/ICCV.2017.74
Tjoa, E., & Guan, C. (2021). A survey on explainable artificial intelligence (XAI): Toward medical XAI. IEEE Transactions on Neural Networks and Learning Systems, 32(11), 4793–4813. https://doi.org/10.1109/TNNLS.2020.3027314
Tuballa, M. L., & Abundo, M. L. (2016). A review of the development of smart grid technologies. Renewable and Sustainable Energy Reviews, 59, 710–725. https://doi.org/10.1016/j.rser.2016.01.011
Venable, J., Pries-Heje, J., & Baskerville, R. (2016). FEDS: A framework for evaluation in design science research. European Journal of Information Systems, 25(1), 77–89. https://doi.org/10.1057/ejis.2014.36
Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology, 31(2), 841–887. https://doi.org/10.2139/ssrn.3063289