Story Telling Data Sharing ML Experience Applications Ethics
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Define your scope or domain where the use case is relevant or prevalent?
The purpose of this study is to examine implicit gender bias in online students’ reviews of STEM professors. Adopting a social-normative perspective, this study defines implicit bias as gendered words and biased frames in language use as a result of benevolent sexism and the “scientist = male” stereotype.What is your main story?
Implicit gender bias affects the evaluation of female STEM professors. From a social-normative perspective, a stereotypical perceptive of scientists is they are men. Implicit gender bias portrays women as vulnerable, innocent, sweet, friendly, and morally pure. They are “nice girls.” Female STEM professors may be framed stereo typically in online teaching reviews, which negatively impact their careers.
Who are the characters or people in the main story?
I am a professor of computer science. I work very hard to get my tenure and promotion. However, I feel that it is hard for me to convince people that I am as capable as my male colleagues. Students, mostly males, seem to have similar views. They tend to question whether I am knowledgeable and complain that they are not getting quality education from me.
What happens?
I am facing tenure and promotion, and student evaluation will be part of it. Student evaluations from online will also be included. I am concerned that implicit bias in the evaluation will negatively impact my promotion due to my gender.
Where?
Online reputation systems
When?
During work, tenure, and promotion evaluations.
Why?
Online reputation systems are consequential to social relationships and networking online and offline.
Biased media frames embedded in online reviews may prime individuals to adopt similar biased attitudes in decision-making.
Online reviews of teaching sometimes have employment implications for female and male STEM professors.
How?
Despite serious consequences, there is a lack of systematic research on implicit bias in online evaluative reviews. We need to collect data and examine implicit gender bias in online students’ reviews of STEM professors
Example: EduKC
