Utah Board of Higher Education Deep Technology Initiative:
Bringing Fairness in AI to the Forefront of Education

Award Duration

November 1, 2021 to October 31, 2024.

PI and Point of Contact

Bei Wang Phillips (PI, University of Utah)
Associate Professor
School of Computing and Scientific Computing and Imaging Institute
University of Utah
beiwang AT sci.utah.edu
Home page

Arul Mishra (Co-PI, University of Utah)
Full Professor
David Eccles School of Business
University of Utah
arul.mishra AT utah.edu
Faculty Profile

Himanshu Mishra (Co-PI, University of Utah)
Full Professor
David Eccles School of Business
University of Utah
himanshu.mishra AT utah.edu
Faculty Profile

Overview

Artificial Intelligence (AI) systems have been used for predicting different risks -- financial, business, medical, and legal risks -- and have been argued to perform better than human experts. The main focus of AI systems has been to predict accurately, but research shows that sometimes they end up discriminating against protected groups. A lack of focus on equity can adversely impact the well-being of millions of people as well as pose legal and ethical challenges for the organization.

Therefore, to educate students, both at the graduate and undergraduate level, we aim to develop interdisciplinary courses and educational modules on fair AI, respectively within the David Eccles School of Business and the School of Computing at the University of Utah. These courses will include, in particular, fairness modules while considering different types of business and social decisions. These educational modules will be integrated within undergraduate and professional graduate courses, as well as seminars, discussing the importance of making not just accurate but also equitable decisions.

Educational Objectives

Algorithms take decisions autonomously or serve as decision aids to make human decision efficient. However, it is important that algorithms not just be efficient and accurate in their decisions, but they are also fair and equitable to people, irrespective of group affiliation. Therefore, it is imperative to understand the accuracy-equity trade-off inherent in using algorithms in everyday decisions. Two courses will be developed, one in the Business School and the other in the School of Computing, offered at the graduate and undergraduate level, considering the importance of fairness in AI. The aim of the two courses is to understand the use of fair algorithms for social and organizational decisions, especially as it pertains to the steps that must be monitored while algorithms are designed, developed, and deployed.

Educational Objectives Achieved

Fall 2024: CS 3090 - Ethics in Computing (Educational Modules)

Fall 2023: MKTG 6600 - Algorithms for Business Decisions (Educational Modules)

Spring 2023: CS 3960 - Algorithm Fairness in Machine Learning (New Course Development)

The new Algorithm Fairness in Machine Learning course is offered at the undergraduate level at the Kahlert School of Computing. It studies how to ensure, via algorithmic developments, that biases in both the data and the model do not lead to models that treat individuals unfavorably on the basis of race, gender, income, etc. This course complements the existing Ethics in Data Science course as another elective course to discuss ethical issues that may arise from the adoption of AI technologies. The course trains the next generation data scientists for the Utah workforce, who employ, implement, or deploy machine learning tools in the industry.

Spring 2022: MKTG 4650/6650 - Fair Algorithms for Business Decisions (New Course Development)

The new Fair Algorithms for Business Decisions course is offered at the professional graduate level within the David Eccles School of Business. It aims to understand the use of AI algorithms for business decisions, especially as it pertains to the steps that must be monitored while algorithms are designed, developed, and deployed. It trains the next generation of business decision-makers in the organization to create awareness of fair AI for greater employee adoption and to increase consumer trust.

Fall 2022: MKTG 6600 - Algorithms for Business Decisions (Educational Modules)

Publications

Students

Xinyuan Yan (U of Utah, CS PhD student)
Nathaniel Gorski (U of Utah, CS PhD student)

Acknowledgement

This material is based upon work supported or partially supported by USHE under its Deep Technology Initiative.

Any opinions, findings, and conclusions or recommendations expressed in this project are those of author(s) and do not necessarily reflect the views of USHE.

Web page last update: October 1, 2024.