UVM SWiCS 2019: 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/
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://wiki.lfai.foundation/display/DL/Trusted+AI+Committee
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

House Oversight Committee on AI
https://oversight.house.gov/legislation/hearings/facial-recognition-technology-part-1-its-impact-on-our-civil-rights-and
https://oversight.house.gov/legislation/hearings/facial-recognition-technology-part-ii-ensuring-transparency-in-government-use

Podcasts/Tweets referenced/used:
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
https://twitter.com/MatthewBParksSr/status/1133435312921874432

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
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.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. UVM Society of Women in
   Computer Science
   Digital Discrimination:
   Cognitive Bias in Machine
   Learning
   November 7, 2019
   

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2. 2
   Hi! My name is
   Maureen McElaney
   →
   

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3. Digital Discrimination:
   Cognitive Bias in Machine
   Learning
   Tweet at me! @Mo_Mack
   3
   

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4. 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
   4
   @Mo_Mack
   

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5. 5
   @Mo_Mack
   

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6. Examples of bias in machine
   learning.
   6
   @Mo_Mack
   

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7. Google’s Cloud
   Natural
   Language API
   7 Image Credit: #WOCinTech
   @Mo_Mack
   

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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
   

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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
   

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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
    

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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
    

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

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13. 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
    13
    

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14. 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
    

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15. 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
    

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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
    

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17. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
    17
    Black Defendant’s Risk Scores
    

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18. Source: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
    18
    White Defendant’s Risk Scores
    

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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
    @Mo_Mack
    

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

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22. http://gendershades.org/
    

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23. “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.”
    23
    - Joy Boulamwini
    @jovialjoy
    

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24. 24
    http://www.aies-conference.com/wp-content/uploads/2019/01/AIES-19_paper_223.pdf
    

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25. 25
    https://www.youtube.com/watch?v=Af2VmR-iGkY
    @Mo_Mack
    

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26. Solutions?
    What can we do
    to combat bias
    in AI?
    26
    @Mo_Mack
    

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27. 27
    https://www.vox.com/ezra-klein-show-podcast
    

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28. “Coders are the
    most empowered
    laborers that have
    ever existed.”
    28
    - Anil Dash
    @anildash
    

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29. EDUCATION IS
    KEY
    29 Image Credit: #WOCinTech
    @Mo_Mack
    

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30. https://www.nytimes.com/2018/02/12/business/computer-science-
    ethics-courses.html
    

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31. 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?
    31
    @Mo_Mack
    

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32. 32
    https://twitter.com/Neurosarda/status/1084198368526680064
    

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

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34. “Cognitive bias in
    machine learning is
    human bias on
    steroids.”
    34
    - Rediet Abebe
    @red_abebe
    @Mo_Mack
    

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35. 35
    https://twitter.com/MatthewBParksSr/status/1133435312921874432
    

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

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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
    @Mo_Mack
    

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

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39. 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:
    https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai
    

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40. #1 -
    Human
    agency and
    oversight.
    #2 -
    Technical
    robustness
    and safety.
    #3 -
    Privacy and
    data
    governance.
    #4 -
    Transparency
    .
    40
    @Mo_Mack
    #5 -
    Diversity,
    non-
    discrimination
    and fairness.
    #6 -
    Societal and
    environmental
    well-being.
    #7 -
    Accountability
    

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41. 41
    https://wiki.lfai.foundation/display/DL/Trusted+AI+Committee
    

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42. TOOLS TO
    COMBAT BIAS
    42 Image Credit: #WOCinTech
    @Mo_Mack
    

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43. Tool #1:
    AI Fairness
    360 Toolkit
    Open Source Library
    43
    @Mo_Mack
    

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44. http://aif360.mybluemix.net/
    @Mo_Mack
    

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45. http://aif360.mybluemix.net/
    @Mo_Mack
    

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46. Machine Learning
    Pipeline
    In-
    Processing
    Pre-
    Processing
    Post-
    Processing
    46
    Modifying the
    training data.
    Modifying the
    learning
    algorithm.
    Modifying the
    predictions (or
    outcomes.)
    @Mo_Mack
    

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47. http://aif360.mybluemix.net/
    Demos
    @Mo_Mack
    

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48. https://github.com/IBM/AIF360
    AI Fairness 360 Toolkit Public Repo
    48
    @Mo_Mack
    

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49. Tool #2:
    Model Asset
    eXchange
    Open Source Pre-Trained
    Deep Learning Models
    49
    @Mo_Mack
    

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50. Step 1: Find a model
    ...that does what you need
    ...that is free to use
    ...that is performant enough
    50
    @Mo_Mack
    

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51. 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
    51
    @Mo_Mack
    

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52. Step 3: Verify the
    model
    ○ Does it do what you need?
    ○ Is it free to use (license)?
    ○ Is it performant enough?
    ○ Accuracy?
    52
    @Mo_Mack
    

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53. Step 4: Train the model
    53
    @Mo_Mack
    

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54. Step 4: Train the model
    54
    @Mo_Mack
    

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55. 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
    55
    @Mo_Mack
    

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56. Step 6: Consume your
    model
    56
    @Mo_Mack
    

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57. 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
    57
    @Mo_Mack
    

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58. ibm.biz/model-exchange
    58
    @Mo_Mack
    

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59. http://ibm.biz/model-exchange
    Model Asset eXchange (MAX)
    59
    @Mo_Mack
    

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60. Tool #3:
    Data Asset
    eXchange
    Open Source Data Sets
    60
    @Mo_Mack
    

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61. http://ibm.biz/data-exchange
    Data Asset eXchange (DAX)
    61
    @Mo_Mack
    

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62. http://ibm.biz/codait-trusted-ai
    IBM CODAIT Trusted AI Work
    62
    @Mo_Mack
    

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63. 63
    https://www.ajlunited.org/fight
    

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64. 64
    https://www.patreon.com/poetofcode
    

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65. 65
    Photo by rawpixel on Unsplash
    No matter what it is our
    responsibility to build
    systems that are fair.
    

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66. Thank you!
    Slides and sources from this talk...
    Any questions for me? @Mo_Mack
    66
    

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