Understanding AI with IBM Developer - 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/
AI Explainability 360 Toolkit: http://aix360.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://bit.ly/lfai-trust
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

Podcasts/Tweets referenced/used:
Ezra Klein Show interview with Anil Dash: 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

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.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. IBM Developer UK: Understanding AI Mini Conf Digital Discrimination: Cognitive Bias in Machine Learning 21 April, 2020
2. Digital Discrimination: Cognitive Bias in Machine Learning Tweet at me! @Mo_Mack 2
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. Example of bias in machine learning. 4 @Mo_Mack
5. NorthPointe’s COMPAS Algorithm 5 Image Credit: #WOCinTech @Mo_Mack
6. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing May 2016 - Northpointe’s COMPAS Algorithm equivant.com/solutions/inmate-classification 6
7. May 2016 - Northpointe’s COMPAS Algorithm equivant.com/solutions/inmate-classification Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
8. May 2016 - Northpointe’s COMPAS Algorithm equivant.com/solutions/inmate-classification Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
9. May 2016 - Northpointe’s COMPAS Algorithm equivant.com/solutions/inmate-classification Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
10. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing 10 Black Defendant’s Risk Scores
11. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing 11 White Defendant’s Risk Scores
12. 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 12 @Mo_Mack
14. Gender Shades Project February 2018 14 Image Credit: #WOCinTech @Mo_Mack
15. gendershades.org
16. “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.” 16 - Joy Boulamwini @jovialjoy
17. 17 http://www.aies-conference.com/wp-content/uploads/2019/01/AIES-19_paper_223.pdf
18. 18 youtube.com/watch?v=Af2VmR-iGkY @Mo_Mack
19. Solutions? What can we do to combat bias in AI? 19 @Mo_Mack
20. 20 vox.com/ezra-klein-show-podcast
21. “Coders are the most empowered laborers that have ever existed.” 21 - Anil Dash @anildash
22. FIX THE PIPELINE? 22 Image Credit: #WOCinTech @Mo_Mack
23. “Cognitive bias in machine learning is human bias on steroids.” 23 - Rediet Abebe @red_abebe @Mo_Mack
24. 24 twitter.com/MatthewBParksSr/status/1133435312921874432
25. January 2019 - New Search Feature on... www.pinterest.com Source: /www.engadget.com/2019/01/24/pinterest-skin-tone-search-diversity/
26. “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.” 26 - Candice Morgan @Candice_MMorgan @Mo_Mack
27. National and Industry Standards 27 Image Credit: #WOCinTech @Mo_Mack
28. 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: ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai
29. #1 - Human agency and oversight. #2 - Technical robustness and safety. #3 - Privacy and data governance. #4 - Transparency . 29 @Mo_Mack #5 - Diversity, non- discrimination and fairness. #6 - Societal and environmental well-being. #7 - Accountability
30. 30 bit.ly/lfai-trust
31. OPEN SOURCE TOOLS 31 Image Credit: #WOCinTech @Mo_Mack
32. Tool #1: AI Fairness 360 Toolkit Open Source Library 32 @Mo_Mack
33. aif360.mybluemix.net @Mo_Mack
34. aif360.mybluemix.net @Mo_Mack
35. Machine Learning Pipeline In- Processing Pre- Processing Post- Processing 35 Modifying the training data. Modifying the learning algorithm. Modifying the predictions (or outcomes.) @Mo_Mack
36. aif360.mybluemix.net Demos @Mo_Mack
37. github.com/IBM/AIF360 AI Fairness 360 Toolkit Public Repo 37 @Mo_Mack
38. Tool #2: AI Explainability 360 Toolkit Open Source Library 38 @Mo_Mack
39. aix360.mybluemix.net @Mo_Mack
40. aix360.mybluemix.net Demos @Mo_Mack
41. github.com/IBM/AIX360 AI Explainability 360 Toolkit Public Repo 41 @Mo_Mack
42. Tool #3: Model Asset eXchange Open Source Pre-Trained Deep Learning Models 42 @Mo_Mack
43. ibm.biz/model-exchange 43 @Mo_Mack
44. ibm.biz/model-exchange Model Asset eXchange (MAX) 44 @Mo_Mack
45. Tool #4: Data Asset eXchange Open Source Data Sets 45 @Mo_Mack
46. /ibm.biz/data-exchange Data Asset eXchange (DAX) 46 @Mo_Mack
47. ibm.biz/codait-trusted-ai IBM CODAIT Trusted AI Work 47 @Mo_Mack
48. Take Control of the Machine Learning Pipeline
49. 49 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
50. © 2018 IBM Corporation 30 April 2019 IBM Data Science Elite 50 Need help? IBM Data Science & AI Elite team. Find them: community.ibm.com/DSE
51. 51 Photo by rawpixel on Unsplash No matter what it is our responsibility to build systems that are fair.
52. Thank you! Any questions for me? @Mo_Mack Find my team: @ibmcodait Trusted AI Projects: ibm.biz/codait-trusted-ai 52