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

Tools/Communities:
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:
https://adweb.clarkson.edu/~awilke/Research_files/EoHB_Wilke_12.pdf

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
https://twitter.com/MatthewBParksSr/status/1133435312921874432

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
https://www.openmic.org/news/2019/5/28/strong-showing-for-amazon-proposal

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
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
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. #CallforCode Hackathon Digital Discrimination: Cognitive Bias in Machine Learning June 20, 2019
2. Digital Discrimination: Cognitive Bias in Machine Learning My name is: Maureen McElaney Tweet at me! @Mo_Mack 2
3. 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
5. Examples of bias in machine learning. 5
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
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
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
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
16. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing 16 Black Defendant’s Risk Scores
17. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing 17 White Defendant’s Risk Scores
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
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/2019/5/28/strong-showing-for-amazon-proposal
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
27. 27 https://www.vox.com/ezra-klein-show-podcast
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
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. 35 https://twitter.com/MatthewBParksSr/status/1133435312921874432
36. January 2019 - New Search Feature on... https://www.pinterest.com Source: https://www.engadget.com/2019/01/24/pinterest-skin-tone-search-diversity/
37. “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
38. TOOLS TO COMBAT BIAS 38 Image Credit: #WOCinTech
39. Tool #1: AI Fairness 360 Toolkit Open Source Library 39
40. http://aif360.mybluemix.net/
41. http://aif360.mybluemix.net/
42. 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
45. Machine Learning Pipeline In- Processing Pre- Processing Post- Processing 45 Modifying the training data. Modifying the learning algorithm. Modifying the predictions (or outcomes.)
47. http://aif360.mybluemix.net/ Demos
48. https://github.com/IBM/AIF360 AI Fairness 360 Toolkit Public Repo 48
49. http://aif360.mybluemix.net/community AI Fairness 360 Toolkit Slack 49
50. Tool #2: Model Asset Exchange Open Source Pre-Trained Deep Learning Models 50
51. Step 1: Find a model ...that does what you need ...that is free to use ...that is performant enough 51
52. 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
53. Step 3: Verify the model ◦ Does it do what you need? ◦ Is it free to use (license)? ◦ Is it performant enough? ◦ Accuracy? 53
54. Step 4: Train the model 54
55. Step 4: Train the model 55
56. 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
57. Step 6: Consume your model 57
58. 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
59. ◦ 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
60. ibm.biz/model-exchange 60
61. http://ibm.biz/model-exchange http://ibm.biz/max-slack Model Asset eXchange (MAX) 61 June 8, 2019 / © 2019 IBM Corporation
62. UPDATE TO THE GENDER SHADES PROJECT 62 Image Credit: #WOCinTech
63. 63 http://www.aies-conference.com/wp-content/uploads/2019/01/AIES-19_paper_223.pdf
64. 64 FAT* 2018: Joy Buolamwini - Intersectional Accuracy Disparities in Commercial Gender Classification https://www.youtube.com/watch?v=Af2VmR-iGkY
65. 65 https://www.ajlunited.org/fight
66. 66 Photo by rawpixel on Unsplash No matter what it is our responsibility to build systems that are fair.
67. Enter the Call for Code https://developer.ibm.com/callforcode/ Slides http://bit.ly/callforcode-biasinai Any questions? @Mo_Mack 67