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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

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More from the Authors
  • Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. By: Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh and Himabindu Lakkaraju
  • "How Do I Fool You?": Manipulating User Trust via Misleading Black Box Explanations By: Himabindu Lakkaraju and Osbert Bastani
  • Human Decisions and Machine Predictions By: Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig and Sendhil Mullainathan
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