Rachel Thomas is an American computer scientist and founding Director of the Center for Applied Data Ethics at the University of San Francisco.

Thomas joined Exelon as a quantitative analyst, where she scraped internet data and built models to provide information to energy traders.

[9] Whilst there is a considerable recruitment demand for artificial intelligence researchers, Thomas has argued that even though these careers have traditionally required a PhD, access to supercomputers and large data sets, these are not essential prerequisites.

[13][14] Alongside her academic career, Thomas has called for more diverse workforces to prevent bias in systems using artificial intelligence.

[9][15] She believes that there should be more people from historically underrepresented groups working in tech to mitigate some of the harms that certain technologies may cause as well as to ensure that the systems created benefit all of society.

The article was published in the Boston Review, titled "Medicine's Machine Learning Problem As Big Data tools reshape health care, biased datasets and unaccountable algorithms threaten to further disempower patients.

"[5] Thomas believes[5] AI's "cool and exclusive aura" needs to be broken in order to unlock it for outsiders, and to make it accessible to those with non-traditional and non-elite backgrounds.