Burlington Data Scientists: 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

Sources:

Podcasts
https://leanin.org/podcast-episodes/siri-is-artificial-intelligence-biased
https://art19.com/shows/the-ezra-klein-show/episodes/663fd0b7-ee60-4e3e-b2cb-4fcb4040eef1

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, Center for Open-Source Data and AI Technologies (CODAIT) @Mo_Mack December 19, 2018 / © 2018 IBM Corporation
2. HELLO! I am Maureen McElaney You can find me at: @Mo_Mack 2
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
18. 18 Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
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. January 2019 - Amazon Rekognition https://aws.amazon.com/rekognition/ 22 Source: https://www.openmic.org/news/2019/1/16/halt-rekognition
23. Joy Buolamwini, Algorithmic Justice League Gender Shades Project Released February 2018 23
25. “ We have entered the age of automation overconfident, yet underprepared. 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.” - @jovialjoy 25
26. SOLUTIONS? 26 What can we do to combat bias in AI?
27. 27 https://art19.com/shows/the-ezra-klein-show/episodes/663fd0b7-ee60-4e3e-b2cb-4fcb4040eef1
28. “ Coders are the most empowered laborers that have ever existed." - @anildash 28
29. EDUCATION IS KEY 29 Image Credit: #WOCinTech
30. 30 https://www.nytimes.com/2018/02/12/business/computer-science-ethics-courses.html
31. QUESTIONS POSED TO STUDENTS ◦ 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. Rediet Abebe, Black in AI “Cognitive bias in machine learning is human bias on steroids.” - @red_abebe 34
35. January 2019 - New Search Feature on https://www.pinterest.com/ 35 Source: https://www.engadget.com/2019/01/24/pinterest-skin-tone-search-diversity/
36. “ "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." - Pinterest 36
37. TOOLS TO COMBAT BIAS 37 Image Credit: #WOCinTech
38. TOOL #1: AI Fairness 360 Toolkit Open Source Library
39. http://aif360.mybluemix.net/
41. 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 41
44. Machine Learning Pipeline In- Processing Pre- Processing Post- Processing 44 Modifying the training data. Modifying the learning algorithm. Modifying the predictions (or outcomes.)
46. DEMO http://aif360.mybluemix.net/
47. https://github.com /IBM/AIF360 AI Fairness 360 Toolkit Public Repo 47
48. http://aif360.mybluemix. net/community AI Fairness 360 Slack Community 48
49. TOOL #2: Model Asset Exchange Open Source Pre-Trained Deep Learning Models
50. http://ibm.biz/model-exchange
51. http://ibm.biz/model-exchange
52. https://github.com/IBM/MAX-Image-Segmenter-Web-App
53. https://github.com/IBM/MAX-Image-Segmenter-Web-App
54. http://ibm.biz/ model-exchange Model Asset eXchange 54
55. TOOL #3: Diversity in Faces Dataset Diverse dataset available to the global research community.
56. 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. 56
57. https://www.research.ib m.com/artificial-intellige nce/trusted-ai/diversity-i n-faces/#access Diversity in Faces Dataset 57
58. UPDATE TO THE GENDER SHADES PROJECT 58 Image Credit: #WOCinTech
59. 59 http://www.aies-conference.com/wp-content/uploads/2019/01/AIES-19_paper_223.pdf
60. 60 https://www.ajlunited.org/fight
61. 61 Photo by rawpixel on Unsplash
62. THANKS! Slides and Links: http://bit.ly/btvds-digital-discrimination Any questions? You can find me at: @Mo_Mack 62