Red Hat DevNation - 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
IBM's Data Science and AI Elite Team: http://community.ibm.com/DSE
Watson Studio: https://www.ibm.com/cloud/watson-studio
Watson Machine Learning: https://www.ibm.com/cloud/machine-learning
Watson OpenScale: https://www.ibm.com/cloud/watson-openscale/

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

Northpointe/Equivant's COMPAS - Recidivism Score Algorithm
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 articles 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. Red Hat DevNation Digital Discrimination: Cognitive Bias in Machine Learning June 27, 2019
2. Digital Discrimination: Cognitive Bias in Machine Learning My name is: Maureen McElaney Tweet at me! @Mo_Mack 2 @Mo_Mack
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 @Mo_Mack
4. 4 @Mo_Mack
5. Example of bias in machine learning. 5 @Mo_Mack
6. NorthPointe’s COMPAS Algorithm 6 Image Credit: #WOCinTech
7. 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 7 @Mo_Mack
8. 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 @Mo_Mack
9. 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 @Mo_Mack
10. 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 @Mo_Mack
11. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing 11 Black Defendant’s Risk Scores @Mo_Mack
12. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing 12 White Defendant’s Risk Scores @Mo_Mack
13. 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 13 @Mo_Mack
14. 14 @Mo_Mack
15. Gender Shades Project 15 Image Credit: #WOCinTech
16. Joy Buolamwini, Algorithmic Justice League Gender Shades Project Released February 2018 16 @Mo_Mack
18. “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.” 18 - Joy Boulamwini @jovialjoy
19. Solutions? What can we do to combat bias in AI? 19 @Mo_Mack
20. 20 https://www.vox.com/ezra-klein-show-podcast @Mo_Mack
21. “Coders are the most empowered laborers that have ever existed.” 21 - Anil Dash @anildash
22. EDUCATION IS KEY 22 Image Credit: #WOCinTech
23. https://www.nytimes.com/2018/02/12/business/computer-science- ethics-courses.html @Mo_Mack
24. 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? 24 @Mo_Mack
25. 25 https://twitter.com/Neurosarda/status/1084198368526680064
26. FIX THE PIPELINE? 26 Image Credit: #WOCinTech
27. “Cognitive bias in machine learning is human bias on steroids.” 27 - Rediet Abebe @red_abebe
28. 28 https://twitter.com/MatthewBParksSr/status/1133435312921874432 @Mo_Mack
29. TOOLS TO COMBAT BIAS 29 Image Credit: #WOCinTech
30. Tool #1: AI Fairness 360 Toolkit Open Source Library 30 @Mo_Mack
31. http://aif360.mybluemix.net/ @Mo_Mack
32. http://aif360.mybluemix.net/ @Mo_Mack
33. 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 33 @Mo_Mack
34. @Mo_Mack
35. @Mo_Mack
36. Machine Learning Pipeline In- Processing Pre- Processing Post- Processing 36 Modifying the training data. Modifying the learning algorithm. Modifying the predictions (or outcomes.) @Mo_Mack
37. @Mo_Mack
38. http://aif360.mybluemix.net/ Demos @Mo_Mack
39. https://github.com/IBM/AIF360 AI Fairness 360 Toolkit Public Repo 39 @Mo_Mack
40. http://aif360.mybluemix.net/community AI Fairness 360 Toolkit Slack 40 @Mo_Mack
41. Tool #2: Model Asset Exchange Open Source Pre-Trained Deep Learning Models 41 @Mo_Mack
42. Step 1: Find a model ...that does what you need ...that is free to use ...that is performant enough 42 @Mo_Mack
43. 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 43 @Mo_Mack
44. Step 3: Verify the model ◦ Does it do what you need? ◦ Is it free to use (license)? ◦ Is it performant enough? ◦ Accuracy? 44 @Mo_Mack
45. Step 4: Train the model 45 @Mo_Mack
46. Step 4: Train the model 46 @Mo_Mack
47. 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 47 @Mo_Mack
48. Step 6: Consume your model 48 @Mo_Mack
49. 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 49 @Mo_Mack
50. ◦ 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 50 Model Asset Exchange @Mo_Mack
51. ibm.biz/model-exchange 51 @Mo_Mack
52. http://ibm.biz/model-exchange http://ibm.biz/max-slack Model Asset eXchange (MAX) 52 @Mo_Mack
53. Take Control of the Machine Learning Pipeline 53 @Mo_Mack
54. 54 IBM’s AI Portfolio Everything you need for Enterprise AI, on any cloud Watson Studio Watson Machine Learning Watson AI OpenScale Build Deploy Manage Interact with Pre-built AI Services Watson Application Services Unify on a Multicloud Data Platform IBM Cloud Private for Data AI Open Source Frameworks @Mo_Mack
55. © 2018 IBM Corporation 30 April 2019 IBM Data Science Elite 55 Need help? IBM Data Science & AI Elite team. Find them: community.ibm.com/DSE @Mo_Mack
56. UPDATE TO THE GENDER SHADES PROJECT 56 Image Credit: #WOCinTech
57. 57 http://www.aies-conference.com/wp-content/uploads/2019/01/AIES-19_paper_223.pdf @Mo_Mack
58. 58 FAT* 2018: Joy Buolamwini - Intersectional Accuracy Disparities in Commercial Gender Classification https://www.youtube.com/watch?v=Af2VmR-iGkY @Mo_Mack
59. 59 Photo by rawpixel on Unsplash No matter what it is our responsibility to build systems that are fair. @Mo_Mack
60. 60 @Mo_Mack
61. Enter the Call for Code https://ibm.biz/BdzPJn Slides http://bit.ly/redhat-biasinai Any questions? @Mo_Mack 61