FIGHT AI BIAS WITH… BIAS

I know, the title of this talk is like saying the only way to stop a bad Terminator is to use a good Terminator but hear me out. Human biases influence the outputs of an Al model. Al amplifies bias and socio- technical harms impact fairness, adoption, safety, and the well being, These harms disproportionately affect legally protected classes of individuals and groups in the United States.
It's so fitting that this year's theme for International Women's day was #BreakTheBias so join Noble as he returns to Strangeloop to expand on the topic of bias, deconstructs techniques to de-bias datasets by example for building intelligent systems that are fair and equitable while increasing trust and adoption.

Transcript

1. Fighting bias…with bias
   Noble Ackerson
   Dir. of Product (AI/ML) @ Ventera Corporation
   

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3. Components of Responsible use of AI
   3
   HCML
   Fairness &
   Bias
   XAI Privacy &
   Data Risk
   Security
   

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4. If AI is to be ambient,
   it better be fair
   Fairness in AI
   

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5. Alexa not recognizing female voices
   or accents from a group of people
   Quantifying Fairness in AI
   Quality of service Opportunities withheld
   Job/loan screening systems excluding
   individuals or groups of individuals
   Traumatized data
   Trauma from collective
   psychological, emotional and
   cognitive distress experienced by an
   unprivileged class
   via IBM Research
   AI Fairness 360
   aif360.mybluemix.net
   

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6. We propagate our
   bias in the intelligent
   solutions we build
   twitter.com/nobleackerson medium.com/@nobleackerson
   

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7. Photo by Noble Ackerson
   

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8. Credit: Twitter | Starbucks
   

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9. Credit: New York Times
   

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10. Photo by Steve Helber | Credit: AP
    

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11. Runaway Feedback Loops in Predictive Policing
    PredPol algorithms are already used by
    police departments in California, Florida,
    Maryland, and a few other States.
    Source: Conference on Fairness, Accountability, and Transparency
    

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12. Bias in a Machine Learning context
    Adapted from: Cornell University ARXIV research 12
    Algorithm
    02
    User Interaction
    03
    01
    Data
    Behavioral Bias
    Presentation Bias
    Linking Bias
    Selection Bias
    Historical Bias
    Aggregation Bias
    Temporal Bias
    Social Bias
    Popularity Bias
    Ranking Bias
    Emergent Bias
    Resource: Catalog of Biases
    

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13. Weʼre all impacted
    Data team, Researchers &
    Engineers
    Increase Data Understanding
    Create Better Algorithms
    Improve Performance
    Produce Robust Models
    Policy & Compliance
    Increase Trust
    Bias & Transparency
    Compliance and Regulation
    Reporting
    Consumers/End Users
    Calibrated Trust
    Bias & Transparency
    Understand Impact
    Reporting & Analyses
    Increase adoption
    13
    

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14. Policy & Compliance
    14
    ❏ Increase trust by being responsible
    ❏ Explaining bias for Compliance and regulation
    ❏ Reporting
    

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15. Gaining regulatory trust
    increases innovation
    Demonstrating human-centered
    stewardship of user data, responsible
    use of production machine learning
    reduces overregulation.
    

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16. Example: Amazon same-day delivery coverage
    Source: https://www.bloomberg.com/graphics/2016-amazon-same-day/
    16
    

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17. 17
    Roxbury, Boston
    Redlining
    

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18. What is Explainable AI ?
    Input Feature
    1. Classification
    2. Prediction
    3. Recommendation
    Black Box
    Input Feature Explained
    model
    1. Classification
    2. Prediction
    3. Recommendation
    1. Which features dominant?
    2. How do features relate?
    3. Why the model predicts this?
    DiffusionBee prompt “cyberpunk, synthwave, oracle explaining her
    prediction to an empty void”
    

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19. You can’t debias
    that what you
    can’t explain
    Regulators are increasingly
    requiring model explanations
    when things go wrong with
    deployed learned models.
    19
    SHapley Additive exPlanations (SHAP)
    

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20. Race
    Skin color
    Biometrics
    Religion
    Sexual Orientation
    Socioeconomic
    status
    Income
    Country of origin
    Questions to consider when looking for bias
    1. Does your use-case or product
    specifically use protected classes?
    2. Does your use-case use data
    correlated with protected classes?
    3. Could your use-case negatively
    impact an individual's economic or
    other important life opportunities?
    20
    

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21. Policy and compliance
    ✓ Increase trust by being responsible
    ✓ Explaining for Compliance and regulation
    ✓ Reporting
    twitter.com/nobleackerson medium.com/@nobleackerson
    

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22. ML team, Researchers & Engineers
    ❏ Increase data & model understanding
    ❏ Create better algorithms
    ❏ Produce robust models
    

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23. Model behavior understanding is critical to fighting bias
    Debug and refine
    model behavior
    Pre-processing
    algorithms
    Deploy and
    Monitor
    Evaluate
    Build and
    Train Model
    Construct and
    Prepare Data
    Define
    Problem
    What-if Analysis
    TF
    23
    Explain Predictions
    Post-processing
    algorithms
    In-processing
    algorithms
    Github IBM AIF360
    pre-process in-process post-process
    

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24. Configure and Load dataset
    Set bias detection options, load dataset, and split between train and test
    

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25. Test Fairness Metric
    Compute fairness metric on original training dataset
    

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26. Fix what's wrong
    Mitigate bias by transforming the original dataset
    

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27. Test Fairness Metric
    Compute fairness metric on transformed training dataset
    

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28. vs
    Midjourney.ai prompt “A face-off between two fighters, one fighting on accuracy and the other fighting for fairness.”
    28
    Accurate Fair
    

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29. Data Scientists, ML engineering, Research
    ✓ Increase data understanding
    ✓ Create better algorithms
    ✓ Produce robust, interpretable models
    twitter.com/nobleackerson medium.com/@nobleackerson
    

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30. Consumers/End Users
    ❏ Increase adoption through calibrated trust
    ❏ Feedback: Context, Choice, and Control
    ❏ Designing for context to understand impact
    ❏ End-user Reporting & Analysis
    

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31. Increase adoption through calibrated trust
    Not enough trust Too much trust
    Calibrated trust
    User wastes time
    searching for additional
    stores in an area
    Trust factor Trust factor
    Trust factor
    User doesn't double
    check before driving
    halfway across town
    User keeps away from a
    store confidently
    identified as busy
    31
    

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32. Recommendations when
    managing end-user feedback
    1. Design in UX mechanisms to
    request feedback.
    2. Build in functionality to respond
    to user feedback.
    3. Interpret and use both implicit
    and explicit user feedback.
    DiffusionBee - Stable Diffusion prompt “depiction of the when things go very wrong with AI”
    

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33. Consumers and End-Users
    ✓ Increase adoption through calibrated trust
    ✓ Feedback: Context, Choice, and Control
    ✓ Designing for context to understand impact
    ✓ End-user Reporting & Analysis
    twitter.com/nobleackerson
    medium.com/@nobleackerson
    

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34. Debiasing techniques
    benefit non-protected
    classes
    Takeaways
    

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35. Some packages lack
    adequate support
    Takeaways
    

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36. Fighting bias…with bias is a balance of
    what was,
    what is,
    and
    what should be
    twitter.com/nobleackerson medium.com/@nobleackerson
    

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