Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power? — mentioned by belter

Fri, 17 Sep 2021 20:44:50 GMT

"Research in machine learning (ML) has primarily argued that models trained on incomplete or biased datasets can lead to discriminatory outputs. In this commentary, we propose moving the research focus beyond biasoriented 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 community — one bias-oriented, the…

Paper and discussion history. A mention is not an endorsement.

Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power? — HN submission

Fri, 17 Sep 2021 20:44:50 GMT

Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?

2 points; 1 discussion reply on HN. Paper and source history. A submission is not an endorsement.

Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power? · HackerPapers discussions

Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power? — mentioned by belter

Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power? — HN submission