PreprintCited by 136Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AISafety Systems Engineering in Autonomy

Four Principles of Explainable Artificial Intelligence

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Phillips, P. Jonathon, Hahn, Carina A., Fontana, Peter C., Broniatowski, David A., Przybocki, Mark A.·2020

Preprint — published as 10.6028/nist.ir.8312 · Paper page

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  • Privacy laws such as GDPR, HIPAA and PIPL all include clauses that state that the handling of healthcare data should be transparent, which means that AI algorithms that work with these data should be transparent and explainable as well.

    “Privacy laws such as GDPR, HIPAA and PIPL all include clauses that state that the handling of healthcare data should be transparent, which means that AI algorithms that work with these data should be transparent and explainable as well.”

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  1. Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B. (2018). Sanity Checks for Saliency Maps. In Bengio, S., Wallach, H., Larochelle, H., Grau- man, K., Cesa-Bianchi, N., and Garnett, R., editors, Advances in Neural Information Processing Systems 31, pages 9505-9515.
  2. Bond, C. F. and DePaulo, B. M. (2006). Accuracy of Deception Judgments Character- izations of Deception. Personality and Social Psychology Review, 10(3):214-234.
  3. Broniatowski, D. A. and Reyna, V. F. (2018). A formal model of fuzzy-trace theroy: Variations on framing effects and the Allais paradox. Decision (Wash D C), 5(4):205- 252.
  4. Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., and Elhadad, N. (2015). Intel- ligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-Day Read- mission. In Proceedings of the 21th ACM SIGKDD International Conference on Knowl- edge Discovery and Data Mining, KDD '15, pages 1721-1730, New York, NY, USA. Association for Computing Machinery. event-place: Sydney, NSW, Australia.
  5. Ferguson, T. (2014). Game Theory. Second edition.
  6. Google LLC (2019). AI Explanations Whitepaper. pages 1-28.
  7. Harvey, N. (1997). Confidence in judgment. Trends in Cognitive Sciences, 1(2):78- 82.
  8. Johansson, P., Hall, L., Sikstr¨ Failure to de- om, S., and Olsson, A. (2005). tect mismatches between intention and outcome in a simple decision task. Science, 310(5745):116-119.
  9. Kroll, J. A., Huey, J., Barocas, S., Felton, E. W., Reidenberg, J. R., Robinson, D. G., and Yu, H. (2017). Accountable Algorithms. University of Pennsylvania Law Review, pages 633-705.
  10. Kveraga, K., Ghuman, A. S., and Bar, M. (2007). Top-down prediction in the cogni- tive brain. Brain and cognition, 65(2):145-168.
  11. Lakkaraju, H., Bach, S. H., and Leskovec, J. (2016). Interpretable Decision Sets: A Joint Framework for Description and Prediction. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16, pages 1675-1684, New York, NY, USA. Association for Computing Machinery. event-place: San Francisco, California, USA.
  12. Molnar, C. (2019). Interpretable Machine Learning. @ChristophMolnar, online edi- tion edition.
  13. Pohl, R. F., editor (2004). Cognitive illusions: A handbook on fallacies and biases in thinking, judgement and memory. Psychology Press.
  14. Reyna, V. F. (2012). A new intuitionism: Meaning, memory, and development in Fuzzy-Trace Theory Valerie. Judgment and Decision Making, 7(3):332-359.
  15. Wilson, T. D. and Bar-Anan, Y. (2008). The unseen mind. Science, 321(5892):1046- 1047.

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Authors
Phillips, P. J., Hahn, C. A., Fontana, P. C., Broniatowski, D. A., & Przybocki, M. A.
Published
2020-08-17
OpenAlex
W4240187149
Cited by
136
References
91
Citations by year
2026: 7 · 2025: 28 · 2024: 22 · 2023: 33 · 2022: 27 · 2021: 17 · 2020: 1
Type
posted content
Language
en
Open access
Licence
cc-by
Funders
Information Technology Laboratory
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
Keywords
explainable AI

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