Reference health
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning
2 of 44 checkable references need attention · checked 2026-07-26
At the dated check, the references listed below either did not resolve in Crossref or DataCite, or carried a retraction notice. Each one is shown with the registry record that put it there.
24 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
References needing attention
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The 42 checked references that resolve
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Semantics derived automatically from language corpora contain human-like biases
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Explaining Explanations: An Overview of Interpretability of Machine Learning
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Foundations for an Empirically Determined Scale of Trust in Automated Systems
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JAABA: interactive machine learning for automatic annotation of animal behavior
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Unequal Representation and Gender Stereotypes in Image Search Results for Occupations
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Principles of Explanatory Debugging to Personalize Interactive Machine Learning
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Investigating statistical machine learning as a tool for software development
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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
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The 24 references without a DOI — listed, not checked
no DOI — not checkedDavid Alvarez-Melis, Hal Daumé, III, Jennifer Wortman Vaughan, and Hanna Wallach. 2019. Weight of Evidence as a Basis for Human-Oriented Explanations. arXiv preprint arXiv:1910.13503 (2019).
no DOI — not checkedJulia Angwin, Jeff Larson, Surya Mattu, and Kirchner Lauren. 2016. Machine Bias: There's software used across the country to predict future criminals. And it's biased against blacks. ProPublica, May 23 (2016), 2016. http://www.propublica.org/article/machine-bias-risk -assessments-in-criminal-sentencing
no DOI — not checkedDean C Barnlund. 2017. A transactional model of communication. In Communication theory, Second edition, C. David Mortensen (Ed.). Routledge, 47--57.
no DOI — not checkedFinale Doshi-Velez and Been Kim. 2017. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017).
no DOI — not checkedFinale Doshi-Velez, Mason Kortz, Ryan Budish, Chris Bavitz, Sam Gershman, David O'Brien, Stuart Schieber, James Waldo, David Weinberger, and Alexandra Wood. 2017. Accountability of AI under the law: The role of explanation. arXiv preprint arXiv:1711.01134 (2017).
no DOI — not checkedDaniel Kahneman. 2011. Thinking, fast and slow. Macmillan.
no DOI — not checkedDaniel Kahneman, Stewart Paul Slovic, Paul Slovic, and Amos Tversky. 1982. Judgment under uncertainty: Heuristics and biases. Cambridge university press.
no DOI — not checkedGuolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 3146--3154. http://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf
no DOI — not checkedBeen Kim, Rajiv Khanna, and Oluwasanmi O Koyejo. 2016. Examples are not enough, learn to criticize! Criticism for Interpretability. In Advances in Neural Information Processing Systems 29, D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett (Eds.). Curran Associates, Inc., 2280--2288. http://papers.nips.cc/paper/6300-examples-are-not-enough-learn-to-criticize-criticism-for-interpretability.pdf
no DOI — not checkedBeen Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres. 2018. Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research), Jennifer Dy and Andreas Krause (Eds.), Vol. 80. PMLR, Stockholmsmässan, Stockholm Sweden, 2668--2677. http://proceedings.mlr.press/v80/kim18d.html
no DOI — not checkedIsaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Sam Gershman, Been Kim, and Finale Doshi-Velez. 2019. Human Evaluation of Models Built for Interpretability. In AAAI Conference on Human Computation and Crowdsourcing (HCOMP).
no DOI — not checkedZachary C Lipton. 2016. The mythos of model interpretability. arXiv preprint arXiv:1606.03490 (2016).
no DOI — not checkedScott M Lundberg, Gabriel G Erion, and Su-In Lee. 2018. Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:1802.03888 (2018).
no DOI — not checkedScott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 4765--4774. http://papers.nips.cc/paper/7062-a-unified-approach -to-interpreting-model-predictions.pdf
no DOI — not checkedPrashan Madumal, Tim Miller, Frank Vetere, and Liz Sonenberg. 2018. Towards a Grounded Dialog Model for Explainable Artificial Intelligence. In First international workshop on socio-cognitive systems at IJCAI 2018. https://arxiv.org/abs/1806.08055
no DOI — not checkedBertram F Malle. 2006. How the mind explains behavior: Folk explanations, meaning, and social interaction. Mit Press.
no DOI — not checkedTim Miller. 2018. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence (2018).
no DOI — not checkedTim Miller, Piers Howe, and Liz Sonenberg. 2017. Explainable AI: Beware of inmates running the asylum or: How I learnt to stop worrying and love the social and behavioural sciences. In IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI).
no DOI — not checkedHarsha Nori, Samuel Jenkins, Paul Koch, and Rich Caruana. 2019. InterpretML: A Unified Framework for Machine Learning Interpretability. arXiv preprint arXiv:1909.09223 (2019).
no DOI — not checkedDonald A Norman. 2014. Some observations on mental models. In Mental models. Psychology Press, 15--22.
no DOI — not checkedForough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Vaughan, and Hanna Wallach. 2018. Manipulating and measuring model interpretability. arXiv preprint arXiv:1802.07810 (2018).
no DOI — not checkedLloyd S Shapley. 1997. A value for n-person games. Classics in game theory (1997), 69.
no DOI — not checkedSarah Tan, Rich Caruana, Giles Hooker, Paul Koch, and Albert Gordo. 2018. Learning global additive explanations for neural nets using model distillation. arXiv preprint arXiv:1801.08640 (2018).
no DOI — not checkedRichard Tomsett, Dave Braines, Dan Harborne, Alun Preece, and Supriyo Chakraborty. 2018. Interpretable to whom? A role-based model for analyzing interpretable machine learning systems. arXiv preprint arXiv:1806.07552 (2018).
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