2021 CJBE Annual Conference

Experience level: 
Intermediate
Intended Audience: 
Faculty
Authors: 
Mike H.M. Teodorescu

Ethical Machine Learning for the Next Generation of Managers

Over the past decade there has been an exponential growth in a need for trained data analysts and machine learning specialists. As teaching students who excel both at technical skills and have strong moral values is core to Ignatian pedagogy, my mission in designing a data science course has been to apply cura personalis to today's ever-growing emphasis on automation. The industry emphasis on automation through machine learning can lead to ethical fading, where the users and writers of software tools do not feel personally responsible for bad decisions made by machine learning, such as discrimination that has been found even in prediction tools by large companies such as Facebook, Amazon, Google. Algorithm-based discrimination can cause massive harm, and students need to learn to use the powerful machine learning tools they gain from courses like mine with responsibility. The skill need for data scientists has extended to business fields adjacent to computer science, such as marketing analytics, accounting, finance, and operations, to name a few. The rush to train enough students to fill the demand in the job market has led to many technical courses that cover principles of data analysis and prediction tools but lack in educating the risks of applying machine learning and the ethics of using such tools which can inadvertently discriminate. In a USAID sponsored research grant while at MIT I studied together with an interdisciplinary group from engineering, computer science, and medicine the effects of low diversity in training data in applications of machine learning to developing country contexts. With USAID I conducted two field trips – one to Ghana and one to India – working with local partners to understand what the regulations of applying machine learning tools are outside the US when applied to outcomes of socioeconomic importance such as hiring, admissions, housing, and how to ensure fair outcomes in a world where we celebrate diversity. Our research has been published by MIT and is either published or in revision and has been incorporated into my courses together with other readings from management and computer science. Highlights from this research are included in my course. My teaching approach combines both practical programming exercises which test for fairness as well as case examples, as well as theoretical concepts grounded in cutting edge management and computer science research. As more and more multinational firms employ analytics, it becomes critical to ensure that firms do not discriminate against their users or employees, or any of their stakeholders. Algorithmic discrimination can be difficult to detect even for the best trained programmers and managers and requires thoughtful organizational processes. The course includes a framework for ethical machine learning and cases of applying machine learning in developing country contexts. Students face ethical dilemmas as “should I make my algorithm higher performance overall, or should I make it fairer across all ethnical groups in my data?” or “should I apply machine learning to this life-changing setting for my users” (cases include medical decisions, hiring decisions, admissions, lending, housing).