Eliminate Discrimination in Data Analytics: Validity Matters

Authors
Sijun Wang-Tang & Zhen (Richard) Tang
Topic
Strategy & General Management
Length
4 pages
Keywords
discrimination
marginalization
data analytics practices
Data Analytics
distortion
bias
Simulation
econometrics
Machine learning
weapon for justice
disadvantaged
Copyright Holder
Sijun Wang-Tang
Student Price
$0.00
Target Audience
Graduate Students
Undergraduate Students
Executive Education
Other Audience

“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.