Frameworks for Explainable Artificial Intelligence in High-Stakes Decision-Making Environments Such as Healthcare and Finance
Keywords:
Explainable Artificial Intelligence, Healthcare, Finance, Interpretability, TransparencyAbstract
Explainable Artificial Intelligence (XAI) has become pivotal in high-stakes decision-making environments like healthcare and finance, where the interpretability of AI-driven decisions directly impacts human lives and economic stability. This paper explores various frameworks for implementing XAI in these critical domains, emphasizing their applicability, strengths, and limitations. It examines how transparency, fairness, and accountability can be achieved through model-agnostic and model-specific approaches, such as SHAP, LIME, and counterfactual reasoning. Moreover, the discussion highlights challenges, including balancing model performance with interpretability and addressing domain-specific nuances. This review consolidates existing knowledge and provides guidance for future research to enhance the trustworthiness and efficacy of AI systems in high-stakes applications.
References
Doshi-Velez, Finale, and Been Kim. "Towards a rigorous science of interpretable machine learning." arXiv preprint arXiv:1702.08608, 2017.
Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1135–1144.
Lundberg, Scott M., and Su-In Lee. "A unified approach to interpreting model predictions." Advances in Neural Information Processing Systems, vol. 30, 2017, pp. 4765–4774.
Wachter, Sandra, Brent Mittelstadt, and Chris Russell. "Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR." Harvard Journal of Law & Technology, vol. 31, no. 2, 2017, pp. 841–887.
Lipton, Zachary C. "The mythos of model interpretability." Communications of the ACM, vol. 61, no. 10, 2018, pp. 36–43.
Samek, Wojciech, Thomas Wiegand, and Klaus-Robert Müller. "Explainable Artificial Intelligence: Understanding, Visualizing, and Interpreting Deep Learning Models." arXiv preprint arXiv:1708.08296, 2017.
Gunning, David. "Explainable Artificial Intelligence (XAI)." Defense Advanced Research Projects Agency (DARPA), 2017.
Holzinger, Andreas, Chris Biemann, Constantinos S. Pattichis, and Douglas B. Kell. "What do we need to build explainable AI systems for the medical domain?" arXiv preprint arXiv:1712.09923, 2017.
Miller, Tim. "Explanation in artificial intelligence: Insights from the social sciences." Artificial Intelligence, vol. 267, 2019, pp. 1–38.
Caruana, Rich, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad. "Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission." Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 1721–1730.
