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Himabindu Lakkaraju
Nber Working Paper Series Human Decisions And
Jens Ludwig
Himabindu Lakkaraju
Overview on Explainable AI Methods Global‐Local‐Ante‐Hoc‐Post‐Hoc
Algorithms, Platforms, and Ethnic Bias: an Integrative Essay
Bayesian Sensitivity Analysis for Offline Policy Evaluation
Designing Alternative Representations of Confusion Matrices to Support Non-Expert Public Understanding of Algorithm Performance
Human Decisions and Machine Predictions*
Examining Wikipedia with a Broader Lens
Counterfactual Predictions Under Runtime Confounding
Faithful and Customizable Explanations of Black Box Models
Using Big Data to Solve Economic and Social Problems
Machine Learning and Development Policy
Productivity and Selection of Human Capital with Machine Learning
Counterfactual Predictions Under Runtime Confounding Supplementary Material
Human Decisions and Machine Predictions
Learning Cost-Effective and Interpretable Treatment Regimes
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical
Top View
Improving College Choice for the Poorest Students Using Behavioral Policy Interventions
Curriculum Vitae April, 2018 JENS LUDWIG
Himabindu Lakkaraju
Sendhil Mullainathan Education Fields of Interest Professional
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Design and Empirical Evaluation of Interactive and Interpretable Machine Learning
Defensible Explanations for Algorithmic Decisions About Writing in Education
Nber Working Paper Series Human Decisions And
Competing Algorithms for Law: Sentencing, Admissions, and Employment Saul Levmore† & Frank Fagan††
Counterfactual Predictions Under Runtime Confounding
A Survey on Explainability: Why Should We Believe the Accuracy of a Model?
Special Edition Special Edition
Applied Machine Learning Pre-Doctoral Research Professional Chicago Booth | Center for Applied AI Professor Sendhil Mullainathan, Director