%0 Journal Article %1 miceli2022studying %A Miceli, Milagros %A Posada, Julian %A Yang, Tianling %D 2022 %I Association for Computing Machinery (ACM) %J Proceedings of the ACM on Human-Computer Interaction %K artificial_intelligence machine_learning %N GROUP %P 1--14 %R 10.1145/3492853 %T Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power? %U https://dl.acm.org/doi/10.1145/3492853 %V 6 %X Research in machine learning (ML) has argued that models trained on incomplete or biased datasets can lead to discriminatory outputs. In this commentary, we propose moving the research focus beyond bias-oriented framings by adopting a power-aware perspective to "study up" ML datasets. This means accounting for historical inequities, labor conditions, and epistemological standpoints inscribed in data. We draw on HCI and CSCW work to support our argument, critically analyze previous research, and point at two co-existing lines of work within our research community \,---\,one bias-centered, the other power-aware. We highlight the need for dialogue and cooperation in three areas: data quality, data work, and data documentation. In the first area, we argue that reducing societal problems to "bias" misses the context-based nature of data. In the second one, we highlight the corporate forces and market imperatives involved in the labor of data workers that subsequently shape ML datasets. Finally, we propose expanding current transparency-oriented efforts in dataset documentation to reflect the social contexts of data design and production.

@article{miceli2022studying, abstract = {Research in machine learning (ML) has argued that models trained on incomplete or biased datasets can lead to discriminatory outputs. In this commentary, we propose moving the research focus beyond bias-oriented framings by adopting a power-aware perspective to "study up" ML datasets. This means accounting for historical inequities, labor conditions, and epistemological standpoints inscribed in data. We draw on HCI and CSCW work to support our argument, critically analyze previous research, and point at two co-existing lines of work within our research community \,---\,one bias-centered, the other power-aware. We highlight the need for dialogue and cooperation in three areas: data quality, data work, and data documentation. In the first area, we argue that reducing societal problems to "bias" misses the context-based nature of data. In the second one, we highlight the corporate forces and market imperatives involved in the labor of data workers that subsequently shape ML datasets. Finally, we propose expanding current transparency-oriented efforts in dataset documentation to reflect the social contexts of data design and production.}, added-at = {2023-04-24T15:06:43.000+0200}, author = {Miceli, Milagros and Posada, Julian and Yang, Tianling}, biburl = {https://www.bibsonomy.org/bibtex/22ca7bdbaca44b8b57889affde225631e/meneteqel}, doi = {10.1145/3492853}, interhash = {4106b111fb6699281b411f02ee305c8e}, intrahash = {2ca7bdbaca44b8b57889affde225631e}, journal = {Proceedings of the {ACM} on Human-Computer Interaction}, keywords = {artificial_intelligence machine_learning}, language = {en-US}, month = jan, number = {{GROUP}}, pages = {1--14}, publisher = {Association for Computing Machinery ({ACM})}, timestamp = {2023-04-24T15:09:18.000+0200}, title = {Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?}, url = {https://dl.acm.org/doi/10.1145/3492853}, volume = 6, year = 2022 }