"This is Transparency to Me" User Insights into Recommendation Algorithm Reporting

Michal Luria

No qfcpx, OSF Preprints from Center for Open Science

Abstract: Supplemental Materials - "This Transparency To Me" Research Prototypes - https://cdt.org/insights/this-is-transparency-to-me-research-prototypes/ Recommendation algorithms by and large determine what people see on social media. But what exactly should platforms share with users about recommendation algorithms that would be meaningful to them? Prior research efforts have looked into frameworks for explainability of algorithms as well as design features across social media platforms that can contribute to their transparency and accountability. We build on these efforts to explore what a recommendation algorithm transparency report may include and how it could present information to users. In this report we conducted a two-part, human-centered co-design research project. In Study 1, participants were invited to participate in several design activities aimed at creating a reflective process about their needs and desires. In Study 2, the same participants were invited to examine the manifestations of their and other users’ own ideas, and to reflect on the strengths and drawbacks these prototypes suggest. Based on the interviews from both studies, we develop guidance about recommendation algorithm reports: what they should include, what aspects they should emphasize, and how they should be communicated.

Date: 2022-10-04
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:qfcpx

DOI: 10.31219/osf.io/qfcpx

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