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
   

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2. Digital Discrimination:
   Cognitive Bias in Machine
   Learning
   Tweet at me! @Mo_Mack
   2
   

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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
   

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4. Example of bias in machine
   learning.
   4
   @Mo_Mack
   

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5. NorthPointe’s
   COMPAS
   Algorithm
   5 Image Credit: #WOCinTech
   @Mo_Mack
   

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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
   

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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
   

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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
   

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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
   

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10. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
    10
    Black Defendant’s Risk Scores
    

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11. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
    11
    White Defendant’s Risk Scores
    

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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
    

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14. Gender
    Shades Project
    February 2018
    14 Image Credit: #WOCinTech
    @Mo_Mack
    

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15. gendershades.org
    

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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
    

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17. 17
    http://www.aies-conference.com/wp-content/uploads/2019/01/AIES-19_paper_223.pdf
    

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18. 18
    youtube.com/watch?v=Af2VmR-iGkY
    @Mo_Mack
    

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19. Solutions?
    What can we do
    to combat bias
    in AI?
    19
    @Mo_Mack
    

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20. 20
    vox.com/ezra-klein-show-podcast
    

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21. “Coders are the
    most empowered
    laborers that have
    ever existed.”
    21
    - Anil Dash
    @anildash
    

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22. FIX THE
    PIPELINE?
    22 Image Credit: #WOCinTech
    @Mo_Mack
    

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23. “Cognitive bias in
    machine learning is
    human bias on
    steroids.”
    23
    - Rediet Abebe
    @red_abebe
    @Mo_Mack
    

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24. 24
    twitter.com/MatthewBParksSr/status/1133435312921874432
    

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25. January 2019 - New Search Feature on...
    www.pinterest.com
    Source: /www.engadget.com/2019/01/24/pinterest-skin-tone-search-diversity/
    

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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
    

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27. National and
    Industry
    Standards
    27 Image Credit: #WOCinTech
    @Mo_Mack
    

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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
    

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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
    

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30. 30
    bit.ly/lfai-trust
    

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31. OPEN SOURCE
    TOOLS
    31 Image Credit: #WOCinTech
    @Mo_Mack
    

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32. Tool #1:
    AI Fairness
    360 Toolkit
    Open Source Library
    32
    @Mo_Mack
    

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33. aif360.mybluemix.net
    @Mo_Mack
    

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34. aif360.mybluemix.net
    @Mo_Mack
    

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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
    

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36. aif360.mybluemix.net
    Demos
    @Mo_Mack
    

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37. github.com/IBM/AIF360
    AI Fairness 360 Toolkit Public Repo
    37
    @Mo_Mack
    

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38. Tool #2:
    AI Explainability
    360 Toolkit
    Open Source Library
    38
    @Mo_Mack
    

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39. aix360.mybluemix.net
    @Mo_Mack
    

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40. aix360.mybluemix.net
    Demos
    @Mo_Mack
    

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41. github.com/IBM/AIX360
    AI Explainability 360 Toolkit
    Public Repo
    41
    @Mo_Mack
    

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42. Tool #3:
    Model Asset
    eXchange
    Open Source Pre-Trained
    Deep Learning Models
    42
    @Mo_Mack
    

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43. ibm.biz/model-exchange
    43
    @Mo_Mack
    

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44. ibm.biz/model-exchange
    Model Asset eXchange (MAX)
    44
    @Mo_Mack
    

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45. Tool #4:
    Data Asset
    eXchange
    Open Source Data Sets
    45
    @Mo_Mack
    

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46. /ibm.biz/data-exchange
    Data Asset eXchange (DAX)
    46
    @Mo_Mack
    

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47. ibm.biz/codait-trusted-ai
    IBM CODAIT Trusted AI Work
    47
    @Mo_Mack
    

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48. Take Control
    of the Machine
    Learning Pipeline
    

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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
    

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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
    

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51. 51
    Photo by rawpixel on Unsplash
    No matter what it is our
    responsibility to build
    systems that are fair.
    

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52. Thank you!
    Any questions for me? @Mo_Mack
    Find my team: @ibmcodait
    Trusted AI Projects: ibm.biz/codait-trusted-ai
    52
    

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