Auditing Partisan Audience Bias within Google Search
Ronald E. Robertson; Shan Jiang; Kenneth Joseph; Lisa Friedland; David Lazer; Christo Wilson · 2018 · Proceedings of the ACM on Human-Computer Interaction
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
There is a growing consensus that online platforms have a systematic influence on the democratic process. However, research beyond social media is limited. In this paper, we report the results of a mixed-methods algorithm audit of partisan…
Excerpt shown for reference under fair use — read the full paper at the publisher.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Investigating Variation in Replicability
Negative / Null Result ReportMany Labs 2: Investigating Variation in Replicability Across Samples and Settings
Failed Experiment ReportGetting Ahead in the Communist Party: Explaining the Advancement of Central Committee Members in China
Negative / Null Result ReportReducing implicit racial preferences: II. Intervention effectiveness across time.
Negative / Null Result ReportTHE IMPACT OF IMMIGRATION ON THE STRUCTURE OF WAGES: THEORY AND EVIDENCE FROM BRITAIN
Negative / Null Result ReportRevisiting the Marshmallow Test: A Conceptual Replication Investigating Links Between Early Delay of Gratification and Later Outcomes
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1145/3274417
