UVM SWiCS 2019: Digital Discrimination: Cognitive Bias in Machine Learning

Tools/Communities:
Center for Open Source Data and Ai Technologies:
https://ibm.biz/codait-trusted-ai
AI Fairness 360 Toolkit: http://aif360.mybluemix.net/
Watson OpenScale: https://www.ibm.com/cloud/watson-openscale/
Model Asset Exchange: http://ibm.biz/model-exchange
Data Asset Exchange: http://ibm.biz/data-exchange
LFAI Trusted AI Committee: https://wiki.lfai.foundation/display/DL/Trusted+AI+Committee
EU Guidelines for Trustworthy AI: https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai

Talk Sources:

Cognitive Bias Definition:
https://adweb.clarkson.edu/~awilke/Research_files/EoHB_Wilke_12.pdf

House Oversight Committee on AI
https://oversight.house.gov/legislation/hearings/facial-recognition-technology-part-1-its-impact-on-our-civil-rights-and
https://oversight.house.gov/legislation/hearings/facial-recognition-technology-part-ii-ensuring-transparency-in-government-use

Podcasts/Tweets referenced/used:
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

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. UVM Society of Women in Computer Science Digital Discrimination: Cognitive Bias in Machine Learning November 7, 2019
2. 2 Hi! My name is Maureen McElaney →
3. Digital Discrimination: Cognitive Bias in Machine Learning Tweet at me! @Mo_Mack 3
4. 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 4 @Mo_Mack
5. 5 @Mo_Mack
6. Examples of bias in machine learning. 6 @Mo_Mack
7. Google’s Cloud Natural Language API 7 Image Credit: #WOCinTech @Mo_Mack
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. October 2017 - Google Natural Language API https://cloud.google.com/natural-language/ 10 Source: https://motherboard.vice.com/en_us/article/j5jmj8/google-artificial-intelligence-bias
11. “We will correct this specific case, and, more broadly, building more inclusive algorithms is crucial to bringing the benefits of machine learning to everyone.” 11
12. NorthPointe’s COMPAS Algorithm 12 Image Credit: #WOCinTech @Mo_Mack
13. 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 13
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. 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
17. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing 17 Black Defendant’s Risk Scores
18. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing 18 White Defendant’s Risk Scores
19. 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 19 @Mo_Mack
21. Gender Shades Project February 2018 21 Image Credit: #WOCinTech @Mo_Mack
22. http://gendershades.org/
23. “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.” 23 - Joy Boulamwini @jovialjoy
24. 24 http://www.aies-conference.com/wp-content/uploads/2019/01/AIES-19_paper_223.pdf
25. 25 https://www.youtube.com/watch?v=Af2VmR-iGkY @Mo_Mack
26. Solutions? What can we do to combat bias in AI? 26 @Mo_Mack
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 @Mo_Mack
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 @Mo_Mack
32. 32 https://twitter.com/Neurosarda/status/1084198368526680064
33. FIX THE PIPELINE? 33 Image Credit: #WOCinTech @Mo_Mack
34. “Cognitive bias in machine learning is human bias on steroids.” 34 - Rediet Abebe @red_abebe @Mo_Mack
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 @Mo_Mack
38. National and Industry Standards 38 Image Credit: #WOCinTech @Mo_Mack
39. EU Ethics Guidelines for Trustworthy Artificial Intelligence According to the Guidelines, trustworthy AI should be: (1) lawful - respecting all applicable laws and regulations (2) ethical - respecting ethical principles and values (3) robust - both from a technical perspective while taking into account its social environment Source: https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai
40. #1 - Human agency and oversight. #2 - Technical robustness and safety. #3 - Privacy and data governance. #4 - Transparency . 40 @Mo_Mack #5 - Diversity, non- discrimination and fairness. #6 - Societal and environmental well-being. #7 - Accountability
41. 41 https://wiki.lfai.foundation/display/DL/Trusted+AI+Committee
42. TOOLS TO COMBAT BIAS 42 Image Credit: #WOCinTech @Mo_Mack
43. Tool #1: AI Fairness 360 Toolkit Open Source Library 43 @Mo_Mack
44. http://aif360.mybluemix.net/ @Mo_Mack
45. http://aif360.mybluemix.net/ @Mo_Mack
46. Machine Learning Pipeline In- Processing Pre- Processing Post- Processing 46 Modifying the training data. Modifying the learning algorithm. Modifying the predictions (or outcomes.) @Mo_Mack
47. http://aif360.mybluemix.net/ Demos @Mo_Mack
48. https://github.com/IBM/AIF360 AI Fairness 360 Toolkit Public Repo 48 @Mo_Mack
49. Tool #2: Model Asset eXchange Open Source Pre-Trained Deep Learning Models 49 @Mo_Mack
50. Step 1: Find a model ...that does what you need ...that is free to use ...that is performant enough 50 @Mo_Mack
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 @Mo_Mack
52. Step 3: Verify the model ◦ Does it do what you need? ◦ Is it free to use (license)? ◦ Is it performant enough? ◦ Accuracy? 52 @Mo_Mack
53. Step 4: Train the model 53 @Mo_Mack
54. Step 4: Train the model 54 @Mo_Mack
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 @Mo_Mack
56. Step 6: Consume your model 56 @Mo_Mack
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 @Mo_Mack
58. ibm.biz/model-exchange 58 @Mo_Mack
59. http://ibm.biz/model-exchange Model Asset eXchange (MAX) 59 @Mo_Mack
60. Tool #3: Data Asset eXchange Open Source Data Sets 60 @Mo_Mack
61. http://ibm.biz/data-exchange Data Asset eXchange (DAX) 61 @Mo_Mack
62. http://ibm.biz/codait-trusted-ai IBM CODAIT Trusted AI Work 62 @Mo_Mack
63. 63 https://www.ajlunited.org/fight
64. 64 https://www.patreon.com/poetofcode
65. 65 Photo by rawpixel on Unsplash No matter what it is our responsibility to build systems that are fair.
66. Thank you! Slides and sources from this talk... Any questions for me? @Mo_Mack 66