An AI with an Agenda: How Our Biases Leak Into Machine Learning (Nebraska.Code 2019)

An AI with an Agenda: How Our Biases Leak Into Machine Learning (Nebraska.Code 2019)

In the glorious AI-assisted future, all decisions are objective and perfect, and there’s no such thing as cognitive biases. That’s why we created AI and machine learning, right? Because humans can make mistakes, and computers are perfect. Well, there’s some bad news: humans make those AIs and machine learning models, and as a result humanity’s biases and missteps can subtly work their way into our AI and models.

All hope isn’t lost, though! In this talk you’ll learn how science and statistics have already solved some of these problems and how a robust awareness of cognitive biases can help with many of the rest. Come learn what else we can do to protect ourselves from these old mistakes, because we owe it to the people who’ll rely on our algorithms to deliver the best possible intelligence!

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Arthur Doler

August 15, 2019
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  1. Arthur Doler @arthurdoler arthurdoler@gmail.com Slides: Handout: AN AI WITH AN

    AGENDA How Our Biases Leak Into Machine Learning bit.ly/art-ai-agenda-necode2019 None
  2. LET’S ALL PLAY A GAME

  3. “THE NURSE SAID”

  4. “THE SOFTWARE ENGINEER SAID”

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  13. REAL CONSEQUENCES

  14. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

  15. http://blog.conceptnet.io/posts/2017/how-to-make-a-racist-ai-without-really-trying/

  16. http://blog.conceptnet.io/posts/2017/how-to-make-a-racist-ai-without-really-trying/ Aylin Caliskan-Islam1 , Joanna J. Bryson1,2, and Arvind Narayanan1,

    2016
  17. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

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  19. SIX CLASSES OF PROBLEM WITH AI/ML

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  23. Class I – Phantoms of False Correlation Class II –

    Specter of Biased Sample Data Class III – Shade of Overly-Simplistic Maximization (Class IV is boring) Class V – The Simulation Surprise Class VI – Apparition of Fairness Class VII – The Feedback Devil
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  30. http://www.tylervigen.com/spurious-correlations - Data sources: Centers for Disease Control & Prevention

    and Internet Movie Database
  31. http://www.tylervigen.com/spurious-correlations - Data sources: National Vital Statistics Reports and U.S.

    Department of Agriculture
  32. http://www.tylervigen.com/spurious-correlations - Data sources: National Spelling Bee and Centers for

    Disease Control & Prevention
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  34. KNOW WHAT QUESTION YOU’RE ASKING UP FRONT

  35. USE CONDITIONAL PROBABILITY OVER CORRELATION

  36. https://versionone.vc/correlation-probability/

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  39. MORTGAGE LENDING ANALYSIS

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  49. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G

  50. Twitter - @quantoidasaurus (Used with permission)

  51. YOUR SAMPLE MIGHT NOT BE REPRESENTATIVE

  52. YOUR DATA MIGHT NOT BE REPRESENTATIVE

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  54. MODELS REPRESENT WHAT WAS THEY DON’T TELL YOU WHAT SHOULD

    BE
  55. FIND A BETTER DATA SET! CONCEPTNET.IO

  56. BUILD A BETTER DATA SET!

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  58. BEWARE SHADOW COLUMNS

  59. MAKE SURE YOUR SAMPLE SET IS REPRESENTATIVE

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  62. IBM’S AI FAIRNESS TOOLKIT

  63. https://aif360.mybluemix.net AI FAIRNESS TOOLKIT

  64. https://aif360.mybluemix.net

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  68. https://aif360.mybluemix.net

  69. https://aif360.mybluemix.net

  70. https://pair-code.github.io/what-if-tool

  71. HAVE A GOOD PROCESS

  72. KEEP IN MIND YOU NEED TO KNOW WHO CAN BE

    AFFECTED IN ORDER TO UN-BIAS
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  74. PRICING ALGORITHMS

  75. Calvano, Calzolari, Denicolò and Pastorello (2018)

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  78. Calvano, Calzolari, Denicolò and Pastorello (2018)

  79. WHAT IF AMAZON BUILT A SALARY TOOL INSTEAD?

  80. THE BRATWURST PROBLEM

  81. HUMANS ARE RARELY SINGLE-MINDED

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  84. https://www.alexirpan.com/2018/02/14/rl-hard.html; Gu, Lillicrap, Sutskever, & Levine, 2016

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  86. MODELS REPRESENT WHAT WAS THEY DON’T TELL YOU WHAT SHOULD

    BE
  87. DON’T TRUST ALGORITHMS TO MAKE SUBTLE OR LARGE MULTI-VARIABLE JUDGEMENTS

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  89. MORE COMPLEX ALGORITHMS THAT INCLUDE OUTSIDE INFLUENCE

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  91. Lehman, Clune, & Misevic, 2018

  92. Cheney, MacCurdy, Clune, & Lipson, 2013

  93. Ellefsen, Mouret, & Clune, 2015 Lehman, Clune, & Misevic, 2018

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  95. BE READY

  96. DON’T CONFUSE THE MAP WITH THE TERRITORY

  97. VERIFY AND CHECK SOLUTIONS DERIVED FROM SIMULATION

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  100. BUT WHAT HAPPENS WITH DIALECTAL LANGUAGE? Blodgett, Green, and O’Connor,

    2016
  101. MANY AI/ML TOOLS ARE TRAINED TO MINIMIZE AVERAGE LOSS

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  103. REPRESENTATION DISPARITY Hashimoto, Srivastava, Namkoong, and Liang, 2018

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  105. CONSIDER PREDICTIVE ACCURACY AS A RESOURCE TO BE ALLOCATED Hashimoto,

    Srivastava, Namkoong, and Liang, 2018
  106. DISTRIBUTIONALLY ROBUST OPTIMIZATION Hashimoto, Srivastava, Namkoong, and Liang, 2018

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  108. LET’S BUILD A PRODUCT WITH OUR TWITTER NLP

  109. WHAT HAPPENS TO PEOPLE WHO USE DIALECT?

  110. PREDICTIVE POLICING

  111. Image via Reddit, Author u/jakeroot

  112. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017

  113. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017

  114. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017

  115. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017

  116. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017

  117. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017

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  119. IGNORE OR ADJUST FOR ALGORITHM-SUGGESTED RESULTS

  120. LOOK TO CONTROL ENGINEERING

  121. By Arturo Urquizo - http://commons.wikimedia.org/wiki/File:PID.svg, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=17633925

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  123. CLASS I - PHANTOMS OF FALSE CORRELATION Know what question

    you’re asking Trust conditional probability over straight correlation
  124. CLASS II - SPECTER OF BIASED SAMPLE DATA Recognize data

    is biased even at rest Make sure your sample set is crafted properly Excise problematic predictors, but beware their shadow columns Build a learning system that can incorporate false positives and false negatives as you find them Try using adversarial techniques to detect bias
  125. CLASS III - SHADE OF OVERLY-SIMPLISTIC MAXIMIZATION Remember models tell

    you what was, not what should be Try combining dependent columns and predicting that Try complex algorithms that allow more flexible reinforcement
  126. CLASS V – THE SIMULATION SURPRISE Don’t confuse the map

    with the territory Always reality-check solutions from simulations
  127. CLASS VI - APPARITION OF FAIRNESS Consider predictive accuracy as

    a resource to be allocated Possibly seek external auditing of results, or at least another team
  128. CLASS VII - THE FEEDBACK DEVIL Ignore or adjust for

    algorithm-suggested results Look to control engineering for potential answers
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  131. MODELS REPRESENT WHAT WAS THEY DON’T TELL YOU WHAT SHOULD

    BE
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  133. OR GET TRAINING

  134. Bootcamps Coursera Udemy Actual Universities

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  136. AI Now Institute Georgetown Law Center on Privacy and Technology

    Knight Foundation’s AI ethics initiative fast.ai Algorithmic Justice League
  137. ABIDE BY ETHICS GUIDELINES

  138. Privacy / Consent Transparency of Use Transparency of Algorithms Ownership

  139. https://www.accenture.com/_acnmedia/PDF-24/Accenture-Universal-Principles-Data-Ethics.pdf

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  141. Slides: Arthur Doler @arthurdoler arthurdoler@gmail.com Handout: bit.ly/art-ai-agenda-necode2019 None