Stanford Seminar - Bias and Representation in Sociotechnical Systems | Summary and Q&A

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May 26, 2021
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Stanford Online
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Stanford Seminar - Bias and Representation in Sociotechnical Systems

TL;DR

Algorithm audits reveal biases in algorithmic content, such as gender and racial representation, which can negatively impact users' sense of belonging and interest in certain fields.

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Questions & Answers

Q: How did the study on biased web interfaces measure participants' sense of belonging?

Participants in the study were asked to rate their intention to enroll in the course, their sense of belonging, and their anticipated success in the course on a seven-point Likert scale after interacting with different versions of the course web page.

Q: What were the main findings of the study on image search results?

The study found that image search results generally underrepresented women and people of color relative to their workforce participation rates in various occupations. The level of underrepresentation varied depending on the occupation, but overall, the representation in search results did not accurately reflect real-world diversity.

Q: Did the study on political partisanship in search media find evidence of bias against conservative perspectives?

No, the study found that search media drew extensively from conservative sources, indicating an absence of widespread bias against conservative content. However, the study did not directly examine the biases within the content produced by these sources.

Q: How did the algorithm audits measure political partisanship in search media?

The algorithm audits collected data on the political affiliations of news websites and assigned a partisanship score based on the ratio of liberals to conservatives who engage with each website. This approach allowed the researchers to analyze the composition of search media in terms of political partisanship.

Summary & Key Takeaways

  • The speaker conducted a study on biased web interfaces, specifically looking at the impact of gender stereotypes on women's sense of belonging in computer science courses.

  • The study found that women exposed to stereotypical web interfaces reported lower intention to enroll, less sense of belonging, and lower anticipated success in the course compared to other participants.

  • Another study examined the representation of marginalized groups in image search results, finding underrepresentation of women and people of color relative to their workforce participation rates.

  • The speaker also conducted an algorithm audit on political partisanship in search media, finding that search results drew extensively from conservative sources and exhibited differences based on incumbency.

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