Explainability, Intepretability, and Transparency

Readings on what is meant by “explainability” (and related terms like “transparency” and “interpretability”) in data science and to what extent acheiving explainabilty (or transparency or interpretability) in algorithms is morally important.

Explainability, Interpretability, and Transparency Reading List PDF

Title	Citation
Transparency in Complex Computational Systems	Creel (2020)
How the Machine “Thinks”: Understanding Opacity in Machine Learning Algorithms	Burrell (2015)
Explainable AI: A Review of Machine Learning Interpretability Methods	Linardatos, Papastefanopoulos, & Kotsiantis (2020)
Transparency’s Ideological Drift	Pozen (2018)
Philosophy of Science at Sea: Clarifying the Interpretability of Machine Learning	Beisbart & Räz (2022)
The Right to an Explanation	Vredenburgh (2021)
Algorithmic and Human Decision Making: For a Double Standard of Transparency	Günther & Kasirzadeh (2022)
The Mythos of Model Interpretability	Lipton (2016)
Epistemic Values in Feature Importance Methods: Lessons from Feminist Epistemology	Hancox-Li & Kumar (2021)
The Fate of Explanatory Reasoning in the Age of Big Data	Cabrera (2020)
Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning	Kaur et al. (2020)
“Explaining” Machine Learning Reveals Policy Challenges	Coyle & Weller (2020)
Why Should I Trust You? Explaining the Predictions of Any Classifier	Ribeiro, Singh, & Guestrin (2016)

References

Beisbart, C., & Räz, T. (2022). Philosophy of science at sea: Clarifying the interpretability of machine learning. Philosophy Compass, 17(6), e12830. https://doi.org/10.1111/phc3.12830

Burrell, J. (2015). How the machine ’thinks:’ understanding opacity in machine learning algorithms. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2660674

Cabrera, F. (2020). The fate of explanatory reasoning in the age of big data. Philosophy &Amp; Technology, 34(4), 645–665. https://doi.org/10.1007/s13347-020-00420-9

Coyle, D., & Weller, A. (2020). "Explaining" machine learning reveals policy challenges. Science, 368(6498), 1433–1434. https://doi.org/10.1126/science.aba9647

Creel, K. A. (2020). Transparency in complex computational systems. Philosophy of Science, 87(4), 568–589. https://doi.org/10.1086/709729

Günther, M., & Kasirzadeh, A. (2022). Algorithmic and human decision making: For a double standard of transparency. AI and Society, 37(1), 375–381. https://doi.org/10.1007/s00146-021-01200-5

Hancox-Li, L., & Kumar, I. E. (2021). Epistemic values in feature importance methods: Lessons from feminist epistemology. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 817–826. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3442188.3445943

Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., & Wortman Vaughan, J. (2020). Interpreting interpretability: Understanding data scientists’ use of interpretability tools for machine learning. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–14. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3313831.3376219

Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020). Explainable AI: A review of machine learning interpretability methods. Entropy, 23, 18. https://doi.org/10.3390/e23010018

Lipton, Z. (2016). The mythos of model interpretability. Communications of the ACM, 61. https://doi.org/10.1145/3233231

Pozen, D. E. (2018). Transparency’s ideological drift. Yale Law Journal, 128, 100–165.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. CoRR, abs/1602.04938. Retrieved from http://arxiv.org/abs/1602.04938

Vredenburgh, K. (2021). The right to explanation. Journal of Political Philosophy, 30(2), 209–229. https://doi.org/10.1111/jopp.12262