Fair Information Access Remote REU

I am providing a remote Research Experience for Undergraduates in summer 2021. Two selected students will work with myself and a Ph.D student remotely, communicating via Slack and Zoom. Due to logistical timelines, the application window is limited; applications are due by April 25, 2021, and selected applicants will be notified by April 30.

In this project, we are trying understand the social impact of information access systems, such as search engines, recommender systems, and other tools that help people find and filter information and products; we are particularly interested in how issues of bias and discrimination affect such systems, such as possible discriminatory patterns in the books or music recommended by the kinds of algorithms that help users find content on platforms like GoodReads and Spotify1. It is funded by NSF grant 17-51278; for more of our work here, see Fair Recommendation.

Work in this project will focus on the following research questions, or related topics based on student interest:

  1. What does it mean for a recommender to be fair, unfair, or biased?
  2. What potentially discriminatory biases are present in the recommender’s input data, algorithmic structure, or output?
  3. How do these biases change over time through the recommender-user feedback loop?

This is a part of our overall, ongoing goal to help make recommenders (and other AI systems) better for the people they affect. Our previous remote REU in Summer 2020 resulted in a co-authored workshop publication.

This is a 10-week paid research opportunity with the People and Information Research Team, from May 24 to July 30. Before the REU starts, we will discuss participants' interests and specific research ideas remotely. Expect to commit 40 hours per week to the research work during that period; the total stipend is $6000.

No research experience is needed, but participants should have some programming experience; the first two courses in a typical computer science introductory sequence, two programming-oriented classes in a statistics or data science program, or comparable experience. Also, due to NSF rules, this opportunity is limited to U.S. citizens, nationals, and permanent residents.

Applications are now closed.

Background

For further background on some of the research in this project, see:


  1. Specific platforms are mentioned only by way of concrete example, and do not imply discriminatory bias by any particular company or product.