Focus Areas

Supported Research

Call for Proposals

Wharton faculty are invited to submit proposals that demonstrate the need for financial support and infrastructure to enhance faculty research, student learning opportunities, and engagement with industry and alumni. Proposals are reviewed on a bi-annual basis. The Spring 2024 application opens on December 8, 2023. Proposals are due by 11:59 p.m. ET on January 19, 2024. If interested in submitting a proposal, please contact


Amy Sepinwall

Artificial Moral Agents

Amy Sepinwall, Associate Professor of Legal Studies and Business Ethics

This project seeks to gain clarity on whether AI can satisfy the requirements of moral agency and how this impacts corporations.

Preprint available:


Ezekiel Emanuel
Ravi Parikh

Using Machine Learning to Improve Medicare’s Risk Adjustment Methodology

Ezekiel J. Emanuel, Diane v.S. Levy and Robert M. Levy University Professor, Professor of Health Care Management, Professor of Medical Ethics and Health Policy
Ravi Parikh, Assistant Professor, Medical Ethics and Health Policy, and Medicine

This project seeks to validate a more accurate risk score with wide adoption potential that can reduce gaming and upcoding systematic over-billing by Medicare Advantage insurers.

Human Resources

Sonny Tambe

The Problems and Perils of Algorithms in Human Resources

Prasanna (Sonny) Tambe, Associate Professor of Operations, Information, and Decisions

This research project conducts an empirical exploration of the relative costs and benefits of using machine learning based tools on video job application data during the hiring process.


Maurice Schweitzer

Developing and Using an AI Negotiator

Maurice E. Schweitzer, Cecilia Yen Koo Professor; Professor of Operations, Information and Decisions

This project will support the development and use of an AI-powered chatbot platform for negotiations.

Etan Green

The Science of Deep Learning: Deep Reinforcement Learning

Etan A. Green, Assistant Professor of Operations, Information, and Decisions

This research project trains artificial intelligence to make optimal offers in negotiations on eBay.


Operations & Productivity

Lorin Hitt
Lynn Wu

AI’s Effect on Innovation and Productivity

Lorin Hitt, Zhang Jindong Professor; Professor of Operations, Information and Decisions
Lynn Wu, Associate Professor of Operations, Information and Decisions

This research explores how AI facilitates innovation by documenting specific cases and mechanisms on when AI technologies should be used to innovate and when they should not, and their implications on demand for different types of labor and productivity.


Ulrich Doraszelski

Biased Technological Change: Implications for Productivity Measurement

Ulrich Doraszelski, Joseph J. Aresty Professor, Professor of Business Economics and Public Policy, Professor of Marketing, Professor of Economics

This project seeks to develop methods for the measurement of productivity that account for these (and other) new technologies with the overarching goal of ensuring that their impact is fully reflected in the aggregate productivity statistics.


Reliability and Pricing in Cloud Computing

Leon Musolff, Assistant Professor, Business Economics and Public Policy

This project focuses on the prevailing “quality differentiation” strategy (in which spot VMs are sold at steep discounts) and its impact on market outcomes.


Dean Knox

An Automated Solution to Causal Inference in Discrete Settings

Dean Knox, Assistant Professor of Operations, Information, and Decisions

The goal of this project is to create a tool to automate casual inference. This tool will reach a broad audience of applied researchers across the social and medical sciences by developing an easy-to-use front-end interface and implement more efficient back-end optimizations. In addition, the project will create a series of data applications to illustrate its ease of use.


Lindsey Cameron

The Trouble with Bots

Lindsey Cameron, Assistant Professor of Management

This project is an inductive two-part multi-sourced qualitative study that focuses on the practices and community around the developers that write bots, scripts and automated programs that are designed to override algorithmic controls and how workers use these technologies to resist and counter algorithmic control.