Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification

Abstract

Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. In this work, we present an approach to evaluate bias present in automated facial analysis algorithms and datasets with respect to phenotypic subgroups. Using the dermatologist approved Fitzpatrick Skin Type classification system, we characterize the gender and skin type distribution of two facial analysis benchmarks, IJB-A and Adience. We find that these datasets are overwhelmingly composed of lighter-skinned subjects (79.6% for IJB-A and 86.2% for Adience) and introduce a new facial analysis dataset which is balanced by gender and skin type. We evaluate 3 commercial gender classification systems using our dataset and show that darker-skinned females are the most misclassified group (with error rates of up to 34.7%). The maximum error rate for lighter-skinned males is 0.8%. The substantial disparities in the accuracy of classifying darker females, lighter females, darker males, and lighter males in gender classification systems require urgent attention if commercial companies are to build genuinely fair, transparent and accountable facial analysis algorithms.

The authors' own abstract, copied from proceedings.mlr.press.

How to cite it

Built from the details listed on this page. Check them against the paper before you submit.

Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research. https://proceedings.mlr.press/v81/buolamwini18a.html

Buolamwini, Joy, et al. "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification." Proceedings of Machine Learning Research, 2018, https://proceedings.mlr.press/v81/buolamwini18a.html.

@article{buolamwini2018gender,
  title = {Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification},
  author = {Joy Buolamwini and Timnit Gebru},
  journal = {Proceedings of Machine Learning Research},
  year = {2018},
  url = {https://proceedings.mlr.press/v81/buolamwini18a.html},
}

What it is

Tested three commercial face-classification products and found error rates of up to 34.7% for darker-skinned women against 0.8% for lighter-skinned men. The study that turned algorithmic bias from a theory into a measured, published fact. Free to read in full.

Topics