We see biases reflected differently in the various forms of AI we’re currently working with.
For example, Resume evaluation systems down-rank women who have attended women’s colleges because of a historical bias against hiring both women, and from those schools, leading to a skewed system where men’s resume’s are more likely to pass muster.
We see data collection biases where certain groups are poorly represented in the collected dataset. For instance, voice recognition systems perform poorly on higher pitched (ie/ female) and Scottish accents because of statistically relative rarity and initial data collection biases (Tatman, 2017).
Leffer, writing for Scientific American, discusses how humans absorb biases from AI. How might the now-banned predictive policing software in Santa Cruz, CA (encouraging increased policing in Black and Brown neighbourhoods, leading to false accusations against people of colour) have changed department officials’ biases over the time it was being used?
Below is an image illustrating how bias is absorbed into AI systems, how it expresses biases at different points, and how these biases amplify and reinforce the prior biases creating a vicious cycle of bias intensification.
Hendrycks, D. (2024). Introduction to AI Safety, Ethics, and Society. Center for AI Safety. https://drive.google.com/file/d/1JN7-ZGx9KLqRJ94rOQVwRSa7FPZGl2OY/view
Tatman, R. (2017). Gender and Dialect Bias in YouTube’s Automatic Captions. In D. Hovy, S. Spruit, M. Mitchell, E. M. Bender, M. Strube, & H. Wallach (Eds.), Proceedings of the First ACL Workshop on Ethics in Natural Language Processing (pp. 53–59). Association for Computational Linguistics. https://doi.org/10.18653/v1/W17-1606
Leffer, L. (2023, October 26). Humans Absorb Bias from AI--And Keep It after They Stop Using the Algorithm. Scientific American. https://www.scientificamerican.com/article/humans-absorb-bias-from-ai-and-keep-it-after-they-stop-using-the-algorithm/
