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.
Interactions with World
Ordering Data as Models to Represent the World
Interpreting Models as Knowledge
| 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) |
| Transparency in Algorithmic and Human Decision-Making: Is There a Double Standard? | Zerilli, Knott, Maclaurin, & Gavaghan (2018) |
| 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
Zerilli, J., Knott, A., Maclaurin, J., & Gavaghan, C. (2018). Transparency in algorithmic and human decision-making: Is there a double standard? Philosophy &Amp; Technology, 32(4), 661–683. https://doi.org/10.1007/s13347-018-0330-6