Supported Academic Research
AIAB is proud to support and partner with faculty across the University in an interdisciplinary effort to advance AI knowledge.
Ethics
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.
Healthcare
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
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.
Negotiation
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.
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
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.
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 develops 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.
Tech
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.
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.