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Publications
  • 2023
  • Chapter
  • The Inequality Reader: Contemporary and Foundational Readings in Race, Class, and Gender

Analyzing Human Decisions and Machine Predictions in Bail Decision Making

By: Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig and Sendhil Mullainathan
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Abstract

BOOK ABSTRACT: Oriented toward the introductory student, The Inequality Reader is the essential textbook for today's undergraduate courses. The editors have assembled the most important classic and contemporary readings about how poverty and inequality are generated and how they might be reduced.

Keywords

Equality and Inequality

Citation

Kleinberg, Jon, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. "Analyzing Human Decisions and Machine Predictions in Bail Decision Making." In The Inequality Reader: Contemporary and Foundational Readings in Race, Class, and Gender. 3rd edition, edited by David B. Grusky and Szonja Szelényi. Routledge, forthcoming.

About The Author

Himabindu Lakkaraju

Technology and Operations Management
→More Publications

More from the Authors

    • 2024
    • Faculty Research

    Fair Machine Unlearning: Data Removal while Mitigating Disparities

    By: Himabindu Lakkaraju, Flavio Calmon, Jiaqi Ma and Alex Oesterling
    • 2024
    • Faculty Research

    Quantifying Uncertainty in Natural Language Explanations of Large Language Models

    By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
    • 2023
    • Advances in Neural Information Processing Systems (NeurIPS)

    Post Hoc Explanations of Language Models Can Improve Language Models

    By: Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh and Himabindu Lakkaraju
More from the Authors
  • Fair Machine Unlearning: Data Removal while Mitigating Disparities By: Himabindu Lakkaraju, Flavio Calmon, Jiaqi Ma and Alex Oesterling
  • Quantifying Uncertainty in Natural Language Explanations of Large Language Models By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
  • Post Hoc Explanations of Language Models Can Improve Language Models By: Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh and Himabindu Lakkaraju
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