Forgetting Practices in the Data Sciences

Michael Muller, Angelika Strohmayer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Citations (Scopus)
20 Downloads (Pure)


HCI engages with data science through many topics and themes. Researchers have addressed biased dataset problems, arguing that bad data can cause innocent software to produce bad outcomes. But what if our software is not so innocent? What if the human decisions that shape our data-processing software, inadvertently contribute their own sources of bias? And what if our data-work technology causes us to forget those decisions and operations? Based in feminisms and critical computing, we analyze forgetting practices in data work practices. We describe diverse beneficial and harmful motivations for forgetting. We contribute: (1) a taxonomy of data silences in data work, which we use to analyze how data workers forget, erase, and unknow aspects of data; (2) a detailed analysis of forgetting practices in machine learning; and (3) an analytic vocabulary for future work in remembering, forgetting, and erasing in HCI and the data sciences.

Original languageEnglish
Title of host publicationCHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
EditorsSimone Barbosa, Cliff Lampe, Caroline Appert , David A. Shamma, Steven Drucker, Julie Williamson, Koji Yatani
Place of PublicationNew York, NY, United States
Number of pages19
ISBN (Electronic)9781450391573
ISBN (Print)9781450391573
Publication statusPublished - 29 Apr 2022
EventACM CHI 2022 - 900 Convention Center Blvd, New Orleans, LA, United States
Duration: 30 Apr 20225 May 2022

Publication series

NameConference on Human Factors in Computing Systems - Proceedings


ConferenceACM CHI 2022
Country/TerritoryUnited States
CityNew Orleans, LA
Internet address


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