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The Hidden Bias Behind Ratings feat. Professor Sora Jun
Manage episode 519027107 series 2818412
How does something as simple as a five-star rating system reveal subtle biases?
When she’s not teaching MBA and undergraduate students at Rice Business, Sora Jun, Assistant Professor of Management – Organizational Behavior, studies the hidden forces that shape how we see and treat one another. Her work explores how our minds process inequality and how even small design choices, like switching from stars to thumbs-up icons, can make systems fairer.
Host Brian Jackson ’21 sits down with Sora to discuss her research on the hidden bias of gig worker ratings, what she loves most about teaching at Rice, the findings of her latest paper and how her background has shaped her work.
Episode Guide:
00:00 Introduction to Sora Jun, Ph.D.
00:58 Journey From Finance To Organizational Behavior
02:20 Impact of Diverse Upbringing on Research
05:05 Teaching Experience and Philosophy
08:52 Research on Bias and Inequality
17:50 Framing Inequality: Advantage vs. Disadvantage
24:34 Exploring Anti-Asian Discrimination
29:17 Future Research Directions
30:56 Teaching Across Different Programs
32:20 Final Thoughts and Takeaways
The Owl Have You Know Podcast is a production of Rice Business and is produced by University FM.
Episode Quotes:
How embracing insecurity helped Dr. Sora on her research
23:32: It actually took me a while to really feel like I had enough legitimacy in standing and studying this in some ways, because I felt like I did not really have the real, like, Asian American experience, given that I have been in so many different circles and I'm hearing so many different stories about what it means to be Asian for different people. But I think really embracing that insecurity almost was useful because I think it just made me dig in deeper and realize that might be part of that Asian experience—feeling like there are so many different kinds of Asian experiences. I'm sure this is similar for other groups as well, but I think I've just become more appreciative of just asking people, like, what's this been like for you? I started to do more qualitative-oriented work because of this, and I think that is helping me sort of reaffirm my own, I guess, standing and studying this topic.
Why the way we talk about inequality matters
16:08 [Sora Jun]: So, framing of inequality matters because even though what is being talked about is logically equivalent for an advantage or disadvantage frame, people understand it to be very different. And then they focus on different, I guess, objects.
On balancing the fairness of the gains of a binary system with the loss of nuance
13:36 [Sora Jun]: I think that's a really tricky part. I do think it is a challenge if we were to imagine changing all these numerical rating scales to dichotomy scales. We would lose a lot of the fine-grained information. So it probably depends a lot on the context. I think from our study, what we were finding was that the ratings using a five-point scale were already quite inflated, so there was not actually a ton of fine-grained information to be had from even the five-point scale information.
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