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* feat(skill): add interpretability workflow * fix(data-scientist): make the causal route clickable * docs(data-scientist): list all interpretation resources
13 lines
1.2 KiB
Markdown
13 lines
1.2 KiB
Markdown
# Interpretability sources
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Primary sources inform method choice; none establishes a universal explanation
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quality threshold.
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| Source | Use |
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| Ribeiro, Singh, and Guestrin, “Why Should I Trust You?” (LIME), KDD 2016, https://doi.org/10.1145/2939672.2939778 | Local surrogate explanations and locality assumptions. |
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| Lundberg and Lee, “A Unified Approach to Interpreting Model Predictions” (SHAP), NeurIPS 2017, https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html | Additive attribution framing; implementation and dependence assumptions must be checked. |
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| Adebayo et al., “Sanity Checks for Saliency Maps,” NeurIPS 2018, https://papers.nips.cc/paper/8160-sanity-checks-for-saliency-maps | Parameter/data randomization checks for some saliency methods. |
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| Molnar, Interpretable Machine Learning, https://christophm.github.io/interpretable-ml-book/ | Living reference for method assumptions and limitations; verify cited methods against original papers. |
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| Mitchell et al., “Model Cards for Model Reporting,” FAT* 2019, https://doi.org/10.1145/3287560.3287596 | Documentation of intended use, performance, and subgroup limitations. |
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