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

OSCON 2019 - Digital Discrimination: Cognitive Bias in Machine Learning

OSCON 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
Data Asset Exchange: http://ibm.biz/data-exchange

Talk Sources:

Cognitive Bias Definition:

House Oversight Committee on AI

Podcasts/Tweets referenced/used:




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

July 17, 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 @Mo_Mack
  2. October 2017 - Google Natural Language API https://cloud.google.com/natural-language/ 7 Source:

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

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

  5. “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
  6. 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 @Mo_Mack
  7. 19

  8. “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
  9. 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 @Mo_Mack
  10. “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 @Mo_Mack
  11. 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 @Mo_Mack
  12. Machine Learning Pipeline In- Processing Pre- Processing Post- Processing 45

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

    ...that is free to use ...that is performant enough 51 @Mo_Mack
  14. 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 @Mo_Mack
  15. Step 3: Verify the model ◦ Does it do what

    you need? ◦ Is it free to use (license)? ◦ Is it performant enough? ◦ Accuracy? 53 @Mo_Mack
  16. 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 @Mo_Mack
  17. 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 @Mo_Mack
  18. ◦ 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 @Mo_Mack
  19. 64 FAT* 2018: Joy Buolamwini - Intersectional Accuracy Disparities in

    Commercial Gender Classification https://www.youtube.com/watch?v=Af2VmR-iGkY @Mo_Mack
  20. 66 Photo by rawpixel on Unsplash No matter what it

    is our responsibility to build systems that are fair.
  21. Thank you! Enter the Call for Code: https://ibm.biz/BdzPJn Meetup TONIGHT:

    Open Source AI for Social Good: www.meetup.com/Portland-Machine-Learning-Meetup Slides and sources from this talk: http://bit.ly/oscon-biasinai Any questions for me? @Mo_Mack 69