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AI Explainability 360: Impact and Design., , , , , , , , , and 10 other author(s). CoRR, (2021)A Formal Framework to Characterize Interpretability of Procedures., , , and . CoRR, (2017)Towards Better Model Understanding with Path-Sufficient Explanations., and . CoRR, (2021)Conditional Gradient Algorithmsfor Rank-One Matrix Approximations with a Sparsity Constraint., and . SIAM Rev., 55 (1): 65-98 (2013)Stochastic Gradient Descent with Biased but Consistent Gradient Estimators., and . CoRR, (2018)Convex approximations to sparse PCA via Lagrangian duality., and . Oper. Res. Lett., 39 (1): 57-61 (2011)Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI., , , , and . HCOMP, page 147-159. AAAI Press, (2022)Probabilistic Rule Induction from Event Sequences with Logical Summary Markov Models., , , and . IJCAI, page 5667-5675. ijcai.org, (2023)Multi-Level Explanations for Generative Language Models., , , , , , , , , and 1 other author(s). CoRR, (2024)AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models., , , , , , , , , and 10 other author(s). J. Mach. Learn. Res., (2020)