Cox Automotive - Digital Discrimination: Cognitive Bias in Machine Learning

Tools/Communities:
AI Fairness 360 Toolkit: http://aif360.mybluemix.net/
Model Asset Exchange: http://ibm.biz/model-exchange
IBM's Public Call for Code Competition: https://callforcode.org/
Maureen's team: IBM's Center for Open Source Data and AI Technologies: http://ibm.biz/codait-projects

Talk Sources:

Podcasts/Tweets
https://leanin.org/podcast-episodes/siri-is-artificial-intelligence-biased
https://art19.com/shows/the-ezra-klein-show/episodes/663fd0b7-ee60-4e3e-b2cb-4fcb4040eef1
https://twitter.com/alexisohanian/status/1087973027055316994

Amazon
https://www.aclu.org/blog/privacy-technology/surveillance-technologies/amazons-face-recognition-falsely-matched-28
https://www.openmic.org/news/2019/1/16/halt-rekognition

Google
https://motherboard.vice.com/en_us/article/j5jmj8/google-artificial-intelligence-bias

COMPAS
https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
https://www.technologyreview.com/s/612775/algorithms-criminal-justice-ai/

Data for Black Lives
http://d4bl.org/about.html
2019 Conference Notes: https://docs.google.com/document/d/1E1mfgTp73QFRmNBunl8cIpyUmDos28rekidux0voTsg/edit?ts=5c39f92e

Gender Shades Project
http://news.mit.edu/2018/study-finds-gender-skin-type-bias-artificial-intelligence-systems-0212
https://www.youtube.com/watch?time_continue=1&v=TWWsW1w-BVo
https://www.ajlunited.org/fight

Other resources referenced in this talk:
https://www.nytimes.com/2018/02/12/business/computer-science-ethics-courses.html
https://www.vox.com/science-and-health/2017/4/17/15322378/how-artificial-intelligence-learns-how-to-be-racist
https://www.engadget.com/2019/01/24/pinterest-skin-tone-search-diversity/

Transcript

1. Cox Automotive Digital Discrimination: Cognitive Bias in Machine Learning May 30th, 2019
2. Digital Discrimination: Cognitive Bias in Machine Learning 2 May 8, 2019 / © 2019 IBM Corporation
3. “A cognitive bias is a systematic pattern of deviation from norm or rationality in judgment. Individuals create their own "subjective social reality" from their perception of the input.” - Wikipedia 3
4. May 8, 2019 / © 2019 IBM Corporation 4
5. Examples of bias in machine learning. 5 May 8, 2019 / © 2019 IBM Corporation
6. Google’s Cloud Natural Language API 6 Image Credit: #WOCinTech
7. October 2017 - Google Natural Language API https://cloud.google.com/natural-language/ 7 Source: https://motherboard.vice.com/en_us/article/j5jmj8/google-artificial-intelligence-bias
8. October 2017 - Google Natural Language API https://cloud.google.com/natural-language/ 8 Source: https://motherboard.vice.com/en_us/article/j5jmj8/google-artificial-intelligence-bias
9. October 2017 - Google Natural Language API https://cloud.google.com/natural-language/ 9 Source: https://motherboard.vice.com/en_us/article/j5jmj8/google-artificial-intelligence-bias
10. “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
11. NorthPointe’s COMPAS Algorithm 11 Image Credit: #WOCinTech
12. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing May 2016 - Northpointe’s COMPAS Algorithm http://www.equivant.com/solutions/inmate- classification 12 May 8, 2019 / © 2019 IBM Corporation
13. May 2016 - Northpointe’s COMPAS Algorithm http://www.equivant.com/solutions/inmate- classification Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing May 8, 2019 / © 2019 IBM Corporation
14. May 2016 - Northpointe’s COMPAS Algorithm http://www.equivant.com/solutions/inmate- classification Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing May 8, 2019 / © 2019 IBM Corporation
15. May 2016 - Northpointe’s COMPAS Algorithm http://www.equivant.com/solutions/inmate- classification Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing May 8, 2019 / © 2019 IBM Corporation
16. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing 16 Black Defendant’s Risk Scores May 8, 2019 / © 2019 IBM Corporation
17. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing 17 White Defendant’s Risk Scores May 8, 2019 / © 2019 IBM Corporation
18. 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
19. 19 May 8, 2019 / © 2019 IBM Corporation
20. Amazon Rekognition 20 Image Credit: #WOCinTech
21. July 2018 - Amazon Rekognition https://aws.amazon.com/rekognition/ 21 Source: https://www.aclu.org/blog/privacy-technology/surveillance-technologies/amazons-face-recognition-falsely-matched-28
22. April 2019 - Amazon Rekognition https://aws.amazon.com/rekognition/ 22 Source: https://www.openmic.org/news/2019/4/4/a-win-for-shareholders-amazon
23. Joy Buolamwini, Algorithmic Justice League Gender Shades Project Released February 2018 23
25. “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
26. Solutions? What can we do to combat bias in AI? 26 May 8, 2019 / © 2019 IBM Corporation
27. 27 https://www.vox.com/ezra-klein-show-podcast May 8, 2019 / © 2019 IBM Corporation
28. “Coders are the most empowered laborers that have ever existed.” 28 - Anil Dash @anildash
29. EDUCATION IS KEY 29 Image Credit: #WOCinTech
30. https://www.nytimes.com/2018/02/12/business/computer-science- ethics-courses.html
31. 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 May 8, 2019 / © 2019 IBM Corporation
32. 32 https://twitter.com/Neurosarda/status/1084198368526680064
33. FIX THE PIPELINE? 33 Image Credit: #WOCinTech
34. “Cognitive bias in machine learning is human bias on steroids.” 34 - Rediet Abebe @red_abebe
35. January 2019 - New Search Feature on... https://www.pinterest.com Source: https://www.engadget.com/2019/01/24/pinterest-skin-tone-search-diversity/
36. “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.” 36 - Candice Morgan @Candice_MMorgan
37. TOOLS TO COMBAT BIAS 37 Image Credit: #WOCinTech
38. Tool #1: AI Fairness 360 Toolkit Open Source Library 38 May 8, 2019 / © 2019 IBM Corporation
39. http://aif360.mybluemix.net/
40. http://aif360.mybluemix.net/
41. 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 41
44. Machine Learning Pipeline In- Processing Pre- Processing Post- Processing 44 Modifying the training data. Modifying the learning algorithm. Modifying the predictions (or outcomes.)
46. http://aif360.mybluemix.net/ Demos
47. https://github.com/IBM/AIF360 AI Fairness 360 Toolkit Public Repo 47 May 8, 2019 / © 2019 IBM Corporation
48. http://aif360.mybluemix.net/community AI Fairness 360 Toolkit Slack 48 May 8, 2019 / © 2019 IBM Corporation
49. Tool #2: Model Asset Exchange Open Source Pre-Trained Deep Learning Models 49 May 8, 2019 / © 2019 IBM Corporation
50. Step 1: Find a model ...that does what you need ...that is free to use ...that is performant enough 50
51. 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 51
52. Step 3: Verify the model ◦ Does it do what you need? ◦ Is it free to use (license)? ◦ Is it performant enough? ◦ Accuracy? 52
53. Step 4: Train the model 53
54. Step 4: Train the model 54
55. 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 55
56. Step 6: Consume your model 56
57. 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 57
58. ◦ 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 58 Model Asset Exchange
59. ibm.biz/model-exchange 59
60. http://ibm.biz/model-exchange http://ibm.biz/max-slack Model Asset eXchange (MAX) 60 May 8, 2019 / © 2019 IBM Corporation
61. UPDATE TO THE GENDER SHADES PROJECT 61 Image Credit: #WOCinTech
62. 62 http://www.aies-conference.com/wp-content/uploads/2019/01/AIES-19_paper_223.pdf
63. 63 https://www.ajlunited.org/fight
64. 64 Photo by rawpixel on Unsplash No matter what it is our responsibility to build systems that are fair.
65. 65 https://callforcode.org/
66. THANKS! Any questions? @Mo_Mack 66