Moving beyond "algorithmic bias is a data problem".
other · Level V
Where this comes from
- Record sourced from PubMed, PMID 33982031.
- Also identified by DOI 10.1016/j.patter.2021.100241 and PMC identifier 8085589.
- Licence recorded as CC BY-NC-ND.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
A surprisingly sticky belief is that a machine learning model merely <i>reflects</i> existing algorithmic bias in the dataset and does not itself contribute to harm. Why, despite clear evidence to the contrary, does the myth of the impartial model still hold allure for so many within our research community? Algorithms are not impartial, and some design choices are better than others. Recognizing how model design impacts harm opens up new mitigation techniques that are less burdensome than comprehensive data collection.