Originally published July 12, 2017
Here is an excerpt:
Algorithmic bias is shaping up to be a major societal issue at a critical moment in the evolution of machine learning and AI. If the bias lurking inside the algorithms that make ever-more-important decisions goes unrecognized and unchecked, it could have serious negative consequences, especially for poorer communities and minorities. The eventual outcry might also stymie the progress of an incredibly useful technology (see “Inspecting Algorithms for Bias”).
Algorithms that may conceal hidden biases are already routinely used to make vital financial and legal decisions. Proprietary algorithms are used to decide, for instance, who gets a job interview, who gets granted parole, and who gets a loan.
The founders of the new AI Now Initiative, Kate Crawford, a researcher at Microsoft, and Meredith Whittaker, a researcher at Google, say bias may exist in all sorts of services and products.
“It’s still early days for understanding algorithmic bias,” Crawford and Whittaker said in an e-mail. “Just this year we’ve seen more systems that have issues, and these are just the ones that have been investigated.”
Examples of algorithmic bias that have come to light lately, they say, include flawed and misrepresentative systems used to rank teachers, and gender-biased models for natural language processing.
The article is here.