Eliminate Discrimination in Data Analytics: Validity Matters

Sijun Wang-Tang & Zhen (Richard) Tang
September 27, 2021
Strategy & General Management
4 pages
discrimination, marginalization, data analytics practices, Data Analytics, distortion, bias, Simulation, econometrics, Machine learning, weapon for justice, disadvantaged
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“Eliminate Discrimination in Data Analytics: Validity Matters” enables the students to be aware of various types of discrimination and marginalization in the data analytics practices and prepares them to mitigate those drawbacks. Contrary to the common advocacy of “letting data speak” in data analytics practices, we emphasize that data can be distorted and biased and that data analytics can be subjected to analysts’ prejudices. To overcome those prejudice and discrimination, we emphasize the valid practices of data analytics by highlighting some common misunderstanding and wrong practices that threaten the analytical validity. This module will help the students visualize such bias through a simulation and introduce them to the latest methods in econometrics and machine learning to counteract the potential discrimination and marginalization. Thus, in the students’ hands, data analytics can truly become a tool for good, a weapon for justice, and a channel for voicing on behalf of the disadvantaged.