An AI with an Agenda: How Our Biases Leak Into Machine Learning (MDC 2020)

Transcript

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

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

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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 https://humansofdata.atlan.com/2017/07/6-sampling-techniques-choose-representative-subset https://blog.socialcops.com/academy/resources/6-sampling-techniques-choose-representative-subset

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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 https://towardsdatascience.com/5-principles-for-big-data-ethics-b5df1d105cd3

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

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

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