Research Summary
Research Summary
Overview
Description
I develop machine learning tools and techniques which are not only accurate but also fair and interpretable so that human decision-makers can leverage them to make better decisions. More specifically, my research addresses the following fundamental questions pertaining to human and algorithmic decision-making:
1. How do we build interpretable models that can aid human decision-making?
2. How do we evaluate the effectiveness of algorithmic predictions and compare them with human decisions?
3. How do we detect and correct underlying biases in human decisions and algorithmic predictions?
These questions have far-reaching implications in domains involving high-stakes decisions such as criminal justice, health care, public policy, business, and education.
1. How do we build interpretable models that can aid human decision-making?
2. How do we evaluate the effectiveness of algorithmic predictions and compare them with human decisions?
3. How do we detect and correct underlying biases in human decisions and algorithmic predictions?
These questions have far-reaching implications in domains involving high-stakes decisions such as criminal justice, health care, public policy, business, and education.
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