Inspiration

  • Major tech companies are rolling out enterprise solutions that leverage their computer vision APIs
  • These tools are being applied to decision making situations that can have significant impact on the individuals and society at large

What it does

  • Addressed the research question: Will image classifiers give different predictions about stock images of doctors split by race and gender?

How we built it

  • Collected 247 images of doctors in two gender and four race categories from ShutterStock
  • Ran images through 4 APIs (Google Vision API, Clarifai API, Amazon Rekognition, Microsoft Azure Computer Vision API) and extracted the predictions of physician/doctor
  • Evaluated differences in prediction confidence levels for doctor/physician across the eight separate groups
  • Manually downloaded and labelled images
  • Used python and jupyter networks for data wrangling, api access,
  • Used R for statistical analysis

Challenges we ran into

  • APIs do not provide their classes for proprietary reasons - presented an issue with respect to missing data

Accomplishments that we're proud of

  • Local Hack Day University of Guelph First Place
  • Great feedback

What we learned

Results of our case study:

Google Vision API

  • Men significantly higher than women
  • Black significantly lower than other three groups
  • Black Female lowest predicted class

Amazon Rekognition API

  • Males significantly higher
  • No bias at race level
  • White Female lowest predicted class

Clarifai API

  • Females significantly higher
  • No bias at race level
  • Black Male lowest predicted class

What's next for Computer Vision API Prejudice Audit

  • Continued investigation into bias in CV APIs
  • More images for a larger sample size

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