A Comprehensive Survey on Explainable Artificial Intelligence (XAI): Challenges, Opportunities, and Future Research Directions
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V2I2P101Keywords:
Explainable Artificial Intelligence (XAI), Artificial Intelligence (AI), InterpretabilityAbstract
Explainable Artificial Intelligence (XAI) has emerged as a critical field in the broader domain of AI, driven by the increasing need for transparency, accountability, and trust in AI systems. This paper provides a comprehensive survey of the current state of XAI, including its challenges, opportunities, and future research directions. We begin by defining XAI and its importance, followed by a detailed exploration of the various techniques and methodologies used in XAI. We then delve into the challenges faced by XAI, including technical, ethical, and practical issues. The paper also highlights the opportunities that XAI presents, such as improved decision-making, enhanced user trust, and better regulatory compliance. Finally, we outline several future research directions that can further advance the field of XAI. This survey aims to serve as a valuable resource for researchers, practitioners, and policymakers interested in the development and application of explainable AI systems
References
[1] 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
[2] Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & 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
[3] Belle, V., & Papantonis, I. (2021). Principles and practice of explainable machine learning. Frontiers in Artificial Intelligence, 4, 688969. https://doi.org/10.3389/frai.2021.688969
[4] Burkart, N., & Huber, M. F. (2021). A survey on the explainability of supervised machine learning. Journal of Artificial Intelligence Research, 70, 245–317. https://doi.org/10.1613/jair.1.12228
[5] Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics, 8(8), 832. https://doi.org/10.3390/electronics8080832
[6] Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608. https://arxiv.org/abs/1702.08608
[7] Du, M., Liu, N., & Hu, X. (2019). Techniques for interpretable machine learning. Communications of the ACM, 63(1), 68–77. https://doi.org/10.1145/3359786
[8] Gunning, D., & Aha, D. W. (2019). DARPA's explainable artificial intelligence (XAI) program. AI Magazine, 40(2), 44–58. https://doi.org/10.1609/aimag.v40i2.2850
[9] Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf
[10] Molnar, C. (2020). Interpretable machine learning. https://christophm.github.io/interpretable-ml-book/
[11] Montavon, G., Samek, W., & Müller, K. R. (2018). Methods for interpreting and understanding deep neural networks. Digital Signal Processing, 73, 1–15. https://doi.org/10.1016/j.dsp.2017.10.011
[12] Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48, 137–141. https://doi.org/10.1007/s11747-019-00710-5
[13] Rudin, C. (2019). Stop explaining black box machine learning models for high-stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1, 206–215. https://doi.org/10.1038/s42256-019-0048-x
[14] Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. Neural Networks, 133, 95–106. https://doi.org/10.1016/j.neunet.2020.11.011
[15] Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
[16] Tjoa, E., & Guan, C. (2021). A survey on explainable artificial intelligence (XAI): Toward medical AI transparency. Computational Intelligence and Neuroscience, 2021, 1–21. https://doi.org/10.1155/2021/3292506
[17] Tomsett, R., Braines, D., Harborne, D., Preece, A., & Chakraborty, S. (2018). Interpretable to whom? A role-based model for analyzing interpretable machine learning systems. arXiv preprint arXiv:1806.07552. https://arxiv.org/abs/1806.07552
[18] van der Waa, J., Nieuwburg, E., Cremers, A., & Neerincx, M. (2021). Evaluating XAI: A unified framework for comparing explainable AI methods. Information Fusion, 81, 24–39. https://doi.org/10.1016/j.inffus.2021.01.008
[19] Wang, D., Yang, Q., Abdul, A., & Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–15. https://doi.org/10.1145/3290605.3300831
[20] Zhou, J., Han, X., Cui, P., & Gao, J. (2021). Foundations and trends in explainable artificial intelligence: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(3), 898–918. https://doi.org/10.1109/TPAMI.2020.3001805
