OS@IBM - The Machine in Sheep’s Clothing: Trust and Transparency in the ML Lifecycle

Tools/Communities:
AI Fairness 360 Toolkit: http://aif360.mybluemix.net/
Model Asset Exchange: http://ibm.biz/model-exchange
IBM's Center for Open Source Data and AI Technologies: http://ibm.biz/codait-projects
IBM's Internal Call for Code Competition: https://w3.ibm.com/developer/callforcode/
IBM's Public Call for Code Competition: https://callforcode.org/

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. Open Source @ IBM Blueprint Talk The Machine in Sheep’s Clothing Trust and Transparency in the ML Lifecycle May 8th, 2019
2. Open Source @ IBM May 8, 2019 / © 2019 IBM Corporation 2 JEFFREY BOREK WW Program Director, Open Technology & IP Mgt. IBM Cognitive Applications jborek@us.ibm.com @jeffborek
3. The Machine in Sheep’s Clothing Trust and Transparency in the ML Lifecycle Maureen McElaney Developer Advocate IBM Center for Open Source Data and AI Technologies mmcelaney@us.ibm.com @Mo_Mack May 8, 2019 / © 2019 IBM Corporation
4. The Machine in Sheep’s Clothing Building Trust and Transparency into the ML Lifecycle 4 May 8, 2019 / © 2019 IBM Corporation
5. May 8, 2019 / © 2019 IBM Corporation 5
6. “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 6
7. May 8, 2019 / © 2019 IBM Corporation 7
8. Examples of bias in machine learning. 8 May 8, 2019 / © 2019 IBM Corporation
9. Google’s Cloud Natural Language API 9 Image Credit: #WOCinTech
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. October 2017 - Google Natural Language API https://cloud.google.com/natural-language/ 11 Source: https://motherboard.vice.com/en_us/article/j5jmj8/google-artificial-intelligence-bias
12. October 2017 - Google Natural Language API https://cloud.google.com/natural-language/ 12 Source: https://motherboard.vice.com/en_us/article/j5jmj8/google-artificial-intelligence-bias
13. “We will correct this specific case, and, more broadly, building more inclusive algorithms is crucial to bringing the benefits of machine learning to everyone.” 13
14. NorthPointe’s COMPAS Algorithm 14 Image Credit: #WOCinTech
15. 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 15 May 8, 2019 / © 2019 IBM Corporation
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 May 8, 2019 / © 2019 IBM Corporation
17. 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
18. 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
19. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing 19 Black Defendant’s Risk Scores May 8, 2019 / © 2019 IBM Corporation
20. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing 20 White Defendant’s Risk Scores May 8, 2019 / © 2019 IBM Corporation
21. 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 21
22. 22 May 8, 2019 / © 2019 IBM Corporation
23. Amazon Rekognition 23 Image Credit: #WOCinTech
24. July 2018 - Amazon Rekognition https://aws.amazon.com/rekognition/ 24 Source: https://www.aclu.org/blog/privacy-technology/surveillance-technologies/amazons-face-recognition-falsely-matched-28
25. April 2019 - Amazon Rekognition https://aws.amazon.com/rekognition/ 25 Source: https://www.openmic.org/news/2019/4/4/a-win-for-shareholders-amazon
26. Joy Buolamwini, Algorithmic Justice League Gender Shades Project Released February 2018 26
28. “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.” 28 - Joy Boulamwini @jovialjoy
29. Solutions? What can we do to combat bias in AI? 29 May 8, 2019 / © 2019 IBM Corporation
30. 30 https://www.vox.com/ezra-klein-show-podcast May 8, 2019 / © 2019 IBM Corporation
31. “Coders are the most empowered laborers that have ever existed.” 31 - Anil Dash @anildash
32. EDUCATION IS KEY 32 Image Credit: #WOCinTech
33. https://www.nytimes.com/2018/02/12/business/computer-science- ethics-courses.html
34. 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? 34 May 8, 2019 / © 2019 IBM Corporation
35. 35 https://twitter.com/Neurosarda/status/1084198368526680064
36. FIX THE PIPELINE? 36 Image Credit: #WOCinTech
37. “Cognitive bias in machine learning is human bias on steroids.” 37 - Rediet Abebe @red_abebe
38. January 2019 - New Search Feature on... https://www.pinterest.com Source: https://www.engadget.com/2019/01/24/pinterest-skin-tone-search-diversity/
39. “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.” 39 - Candice Morgan @Candice_MMorgan
40. TOOLS TO COMBAT BIAS 40 Image Credit: #WOCinTech
41. Tool #1: AI Fairness 360 Toolkit Open Source Library 41 May 8, 2019 / © 2019 IBM Corporation
42. http://aif360.mybluemix.net/
43. http://aif360.mybluemix.net/
44. 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 44
47. Machine Learning Pipeline In- Processing Pre- Processing Post- Processing 47 Modifying the training data. Modifying the learning algorithm. Modifying the predictions (or outcomes.)
49. http://aif360.mybluemix.net/ Demos
50. https://github.com/IBM/AIF360 AI Fairness 360 Toolkit Public Repo 50 May 8, 2019 / © 2019 IBM Corporation
51. http://aif360.mybluemix.net/community AI Fairness 360 Toolkit Slack 51 May 8, 2019 / © 2019 IBM Corporation
52. Tool #2: Model Asset Exchange Open Source Pre-Trained Deep Learning Models 52 May 8, 2019 / © 2019 IBM Corporation
53. Step 1: Find a model ...that does what you need ...that is free to use ...that is performant enough 53
54. 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 54
55. Step 3: Verify the model ◦ Does it do what you need? ◦ Is it free to use (license)? ◦ Is it performant enough? ◦ Accuracy? 55
56. Step 4: Train the model 56
57. Step 4: Train the model 57
58. 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 58
59. Step 6: Consume your model 59
60. 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 60
61. ◦ 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 61 Model Asset Exchange
62. ibm.biz/model-exchange 62
63. http://ibm.biz/model-exchange http://ibm.biz/max-slack Model Asset eXchange (MAX) 63 May 8, 2019 / © 2019 IBM Corporation
64. Dedicated Open Source Efforts IBM Center for Open-Source Data and AI Technologies (CODAIT) 64 May 8, 2019 / © 2019 IBM Corporation
65. Center for Open Source Data and AI Technologies CODAIT aims to make AI solutions dramatically easier to create, deploy, and manage in the enterprise. 40 open source developers! 65 CODAIT codait.org May 8, 2019 / © 2019 IBM Corporation
66. Improving Enterprise AI lifecycle in Open Source
67. 67 Or is it? That’s a lot of open source developers! May 8, 2019 / © 2019 IBM Corporation
68. Active IBM Contributors to Open Source (Committed code on Github in 2018) 68 May 8, 2019 / © 2019 IBM Corporation
69. Active IBM Users of Open Source (Certified to consume and/or contribute open source in 2018) 69 May 8, 2019 / © 2019 IBM Corporation
70. http://ibm.biz/codait-projects IBM Center for Open Source Data and AI Technologies (CODAIT) 70 May 8, 2019 / © 2019 IBM Corporation
71. UPDATE TO THE GENDER SHADES PROJECT 71 Image Credit: #WOCinTech
72. 72 http://www.aies-conference.com/wp-content/uploads/2019/01/AIES-19_paper_223.pdf
73. 73 https://www.ajlunited.org/fight
74. 74 Photo by rawpixel on Unsplash No matter what it is our responsibility to build systems that are fair.
75. 75 https://w3.ibm.com/developer/callforcode/
76. THANKS! Any questions? @Mo_Mack 76