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  • 14 Aug 2017
  • Conference Presentation

Interpretable and Explorable Approximations of Black Box Models

By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Jure Leskovec
  • Format:Print
  • | Language:English
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Citation

Lakkaraju, Himabindu, Ece Kamar, Rich Caruana, and Jure Leskovec. "Interpretable and Explorable Approximations of Black Box Models." Paper presented at the 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), Halifax, NS, Canada, August 14, 2017.
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About The Author

Himabindu Lakkaraju

Technology and Operations Management
→More Publications

More from the Authors

    • 2023
    • Faculty Research

    When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions

    By: Himabindu Lakkaraju and Chiara Farronato
    • May 2022
    • Faculty Research

    Altibbi: Revolutionizing Telehealth Using AI

    By: Himabindu Lakkaraju
    • 2022
    • Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)

    Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis.

    By: Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay and Himabindu Lakkaraju
More from the Authors
  • When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions By: Himabindu Lakkaraju and Chiara Farronato
  • Altibbi: Revolutionizing Telehealth Using AI By: Himabindu Lakkaraju
  • Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. By: Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay and Himabindu Lakkaraju
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