ODSC East - Digital Discrimination: Cognitive Bias in Machine Learning

Tools:
AI Fairness 360 Toolkit: http://aif360.mybluemix.net/
Model Asset Exchange: http://ibm.biz/model-exchange
Image Segmenter Web App: https://github.com/IBM/MAX-Image-Segmenter-Web-App
Diversity in Faces Dataset: https://www.research.ibm.com/artificial-intelligence/trusted-ai/diversity-in-faces/#acces
IBM's Call for Code Competition: https://callforcode.org/

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
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. Digital Discrimination: Cognitive Bias in Machine Learning Maureen McElaney Developer Advocate Twitter: @Mo_Mack Brendan Dwyer Developer Email: brendan.dwyer@ibm.com Center for Open-Source Data and AI Technologies (CODAIT) December 19, 2018 / © 2018 IBM Corporation
2. HELLO! Maureen McElaney You can contact me at: @Mo_Mack 2 Brendan Dwyer You can contact me at: brendan.dwyer@ibm.com
3. Digital Discrimination Cognitive Bias in Machine Learning 3
4. “ “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 4
5. 5 https://twitter.com/alexisohanian/status/1087973027055316994
6. Examples of Cognitive Bias in Machine Learning
7. Google’s Cloud Natural Language API 7 Image Credit: #WOCinTech
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
13. May 2016 - Northpointe’s COMPAS Algorithm http://www.equivant.com/solutions/inmate-classification 13 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 14 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 15 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 16 Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
17. 17 Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Black Defendant’s Risk Scores
18. 18 Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing 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
21. Amazon Rekognition 21 Image Credit: #WOCinTech
22. July 2018 - Amazon Rekognition https://aws.amazon.com/rekognition/ 22 Source: https://www.aclu.org/blog/privacy-technology/surveillance-technologies/amazons-face-recognition-falsely-matched-28
23. April 2019 - Amazon Rekognition https://aws.amazon.com/rekognition/ 23 Source: https://www.openmic.org/news/2019/4/4/a-win-for-shareholders-amazon
24. Joy Buolamwini, Algorithmic Justice League Gender Shades Project Released February 2018 24
26. “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.” 26 - Joy Boulamwini @jovialjoy
27. SOLUTIONS? 27 What can we do to combat bias in AI?
28. 28 https://art19.com/shows/the-ezra-klein-show/episodes/663fd0b7-ee60-4e3e-b2cb-4fcb4040eef1
29. “Coders are the most empowered laborers that have ever existed.” 29 - Anil Dash @anildash
30. EDUCATION IS KEY 30 Image Credit: #WOCinTech
31. 31 https://www.nytimes.com/2018/02/12/business/computer-science-ethics-courses.html
32. QUESTIONS POSED TO STUDENTS ◦ Is the technology fair? ◦ How do you make sure that the data is not biased? ◦ Should machines be judging humans? 32
33. 33 https://twitter.com/Neurosarda/status/1084198368526680064
34. FIX THE PIPELINE? 34 Image Credit: #WOCinTech
35. “Cognitive bias in machine learning is human bias on steroids.” 35 - Rediet Abebe @red_abebe
36. January 2019 - New Search Feature on https://www.pinterest.com/ 36 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
40. 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. DEMO http://aif360.mybluemix.net/
48. https://github.com /IBM/AIF360 AI Fairness 360 Toolkit Public Repo 48
49. http://aif360.mybluemix. net/community AI Fairness 360 Slack Community 49
50. TOOL #2: Model Asset Exchange Open Source Pre-Trained Deep Learning Models
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 Model Asset eXchange 61
62. ibm.biz/max-slack Model Asset eXchange Slack Community 62
63. TOOL #3: Diversity in Faces Dataset Diverse dataset available to the global research community.
64. Diversity in Faces Dataset Studying diversity in faces is complex. The dataset provides a jumping off point for the global research community to further our collective knowledge. 64
65. http://ibm.biz/diversity-dataset Diversity in Faces Dataset 65
66. UPDATE TO THE GENDER SHADES PROJECT 66 Image Credit: #WOCinTech
67. 67 http://www.aies-conference.com/wp-content/uploads/2019/01/AIES-19_paper_223.pdf
68. 68 https://www.ajlunited.org/fight
69. 69 Photo by rawpixel on Unsplash No matter what it is our responsibility to build systems that are fair.
70. THANKS! Slides and Links: Any questions? You can contact Maureen on Twitter @Mo_Mack Or Brendan via email at brendan.dwyer@ibm.com 70