stefanoteso/awesome-explanatory-supervision
List of relevant resources for machine learning from explanatory supervision
This is a curated collection of academic papers focused on "explanatory supervision," a machine learning approach where models learn not just from data, but also from explanations of how they arrive at their predictions. It organizes research on how to feed model explanations as input to improve a model's trustworthiness or performance, and what output to expect from such models. Researchers and practitioners working on building more transparent and understandable AI systems would find this resource valuable.
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Use this if you are researching or developing machine learning models and want to incorporate explicit explanations into the training process to improve model interpretability, reliability, or accuracy.
Not ideal if you are looking for ready-to-use software libraries or practical guides for general machine learning tasks without a specific focus on explanation-driven model training.
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Jul 14, 2025
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