Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Call for Code Hackathon 2019 - Digital Discrimination: Cognitive Bias in Machine Learning

Call for Code Hackathon 2019 - Digital Discrimination: Cognitive Bias in Machine Learning

Enter IBM's Call for Code Competition: https://ibm.biz/BdzPJn
AI Fairness 360 Toolkit: http://aif360.mybluemix.net/
Model Asset Exchange: http://ibm.biz/model-exchange

Talk Sources:

Cognitive Bias Definition:





Data for Black Lives
2019 Conference Notes: https://docs.google.com/document/d/1E1mfgTp73QFRmNBunl8cIpyUmDos28rekidux0voTsg/edit?ts=5c39f92e

Gender Shades Project
MIT Media Lab Overview for the project: https://www.youtube.com/watch?time_continue=1&v=TWWsW1w-BVo
FAT* 2018 Talk about outcomes: https://www.youtube.com/watch?v=Af2VmR-iGkY

Other resources referenced in this talk:

Maureen McElaney

June 20, 2019

More Decks by Maureen McElaney

Other Decks in Technology


  1. A cognitive bias is a systematic pattern of deviation from

    norm or rationality in judgment. People make decisions given their limited resources. Wilke A. and Mata R. (2012) “Cognitive Bias”, Clarkson University 3
  2. 4

  3. October 2017 - Google Natural Language API https://cloud.google.com/natural-language/ 7 Source:

  4. October 2017 - Google Natural Language API https://cloud.google.com/natural-language/ 8 Source:

  5. October 2017 - Google Natural Language API https://cloud.google.com/natural-language/ 9 Source:

  6. “We will correct this specific case, and, more broadly, building

    more inclusive algorithms is crucial to bringing the benefits of machine learning to everyone.” 10
  7. BLACK VS. WHITE DEFENDANTS ◦ Falsely labeled black defendants as

    likely of future crime at twice the rate as white defendants. ◦ White defendants mislabeled as low risk more than black defendants ◦ Pegged Black defendants 77% more likely to be at risk of committing future violent crime 18
  8. 19

  9. “If we fail to make ethical and inclusive artificial intelligence

    we risk losing gains made in civil rights and gender equity under the guise of machine neutrality.” 25 - Joy Boulamwini @jovialjoy
  10. Questions posed to students in these courses... Is the technology

    fair? How do you make sure that the data is not biased? Should machines be judging humans? 31
  11. “By combining the latest in machine learning and inclusive product

    development, we're able to directly respond to Pinner feedback and build a more useful product.” 37 - Candice Morgan @Candice_MMorgan
  12. TYPES OF METRICS ◦ Individual vs. Group Fairness, or Both

    ◦ Group Fairness: Data vs Model ◦ Group Fairness: We’re All Equal vs What You See is What You Get ◦ Group Fairness: Ratios vs Differences 42
  13. Machine Learning Pipeline In- Processing Pre- Processing Post- Processing 45

    Modifying the training data. Modifying the learning algorithm. Modifying the predictions (or outcomes.)
  14. Step 1: Find a model ...that does what you need

    ...that is free to use ...that is performant enough 51
  15. Step 2: Get the code Is there a good implementation

    available? ...that does what you need ...that is free to use ...that is performant enough 52
  16. Step 3: Verify the model ◦ Does it do what

    you need? ◦ Is it free to use (license)? ◦ Is it performant enough? ◦ Accuracy? 53
  17. Step 5: Deploy your model ◦ Adjust inference code (or

    write from scratch) ◦ Package inference code, model code, and pre-trained weights together ◦ Deploy your package 56
  18. Model Asset Exchange The Model Asset Exchange (MAX) is a

    one stop shop for developers/data scientists to find and use free and open source deep learning models ibm.biz/model-exchange 58
  19. ◦ Wide variety of domains (text, audio, images, etc) ◦

    Multiple deep learning frameworks ◦ Vetted and tested code/IP ◦ Build and deploy a model web service in seconds 59 Model Asset Exchange
  20. 64 FAT* 2018: Joy Buolamwini - Intersectional Accuracy Disparities in

    Commercial Gender Classification https://www.youtube.com/watch?v=Af2VmR-iGkY
  21. 66 Photo by rawpixel on Unsplash No matter what it

    is our responsibility to build systems that are fair.