Data Cleaning, Discard Studies, and Discretionary Power

Abstract

Data cleaning is an overlooked yet impactful step in the Artificial Intelligence (AI) development pipeline, leading to negative downstream impacts when performed carelessly. Using Discard Studies as a framework, I propose an ethnographic study of how data practitioners exercise their discretionary power during the data cleaning process, particularly with respect to discarded data. The in-depth knowledge of the data cleaning process gained as a result of this study will allow us to improve guidelines and education on data cleaning for more ethical AI development.