CHI 2022
Conference paper

Forgetting practices in the data sciences

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HCI engages with data science through many topics and themes. Researchers have addressed problems with potentially biased data sampling, arguing that bad data can cause innocent software to produce bad outcomes. But what if our software is not so innocent? What sources of bias do we introduce into our data? Based in feminisms and critical computing, we analyze forgetting practices in data science work practices of planning, finding, cleaning, engineering, curating, and labeling the data of data science. Forgetting practices can weaken the integrity of data and outcomes. We contribute: (1) a taxonomy of data silences in data work in HCI and data science, 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) a proposed analytic vocabulary for future work in remembering, forgetting, and erasing in HCI and the data sciences.


30 Apr 2022


CHI 2022