An AI with an Agenda: How Our Biases Leak Into Machine Learning (Codestock 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!

Transcript

1. Arthur Doler @arthurdoler arthurdoler@gmail.com Slides: Handout: AN AI WITH AN AGENDA How Our Biases Leak Into Machine Learning None
3. LET’S ALL PLAY A GAME
4. “THE NURSE SAID”
5. “THE SOFTWARE ENGINEER SAID”
14. REAL CONSEQUENCES
15. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
16. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
17. 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
18. http://blog.conceptnet.io/posts/2017/how-to-make-a-racist-ai-without-really-trying/
20. SIX CLASSES OF PROBLEM WITH AI/ML
24. Class I – Phantoms of False Correlation Class II – Specter of Biased Sample Data Class III – Shade of Overly-Simplistic Maximization Class V – The Simulation Surprise Class VI – Apparition of Fairness Class VII – The Feedback Devil
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
34. KNOW WHAT QUESTION YOU’RE ASKING UP FRONT
35. USE CONDITIONAL PROBABILITY OVER CORRELATION
36. https://versionone.vc/correlation-probability/
39. MORTGAGE LENDING ANALYSIS
44. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G
45. Twitter - @quantoidasaurus (Used with permission)
46. YOUR SAMPLE MIGHT NOT BE REPRESENTATIVE
47. YOUR DATA MIGHT NOT BE REPRESENTATIVE
49. MODELS REPRESENT WHAT WAS THEY DON’T TELL YOU WHAT SHOULD BE
50. FIND A BETTER DATA SET! CONCEPTNET.IO
51. BUILD A BETTER DATA SET!
53. BEWARE SHADOW COLUMNS
54. RESTRUCTURE YOUR DATA SET PROBABILISTICALLY
55. DATA AUGMENTATION
58. MAKE SURE YOUR SAMPLE SET IS REPRESENTATIVE
61. HAVE A GOOD PROCESS
62. USE ADVERSARIAL ALGORITHMS TO DETECT BIAS IN YOUR MODELS
63. KEEP IN MIND YOU NEED TO KNOW WHO CAN BE AFFECTED IN ORDER TO UN-BIAS
65. PRICING ALGORITHMS
66. Calvano, Calzolari, Denicolò and Pastorello (2018)
69. Calvano, Calzolari, Denicolò and Pastorello (2018)
70. WHAT IF AMAZON BUILT A SALARY TOOL INSTEAD?
71. THE BRATWURST PROBLEM
72. HUMANS ARE RARELY SINGLE-MINDED
75. https://www.alexirpan.com/2018/02/14/rl-hard.html; Gu, Lillicrap, Sutskever, & Levine, 2016
77. MODELS REPRESENT WHAT WAS THEY DON’T TELL YOU WHAT SHOULD BE
78. DON’T TRUST ALGORITHMS TO MAKE SUBTLE OR LARGE MULTI-VARIABLE JUDGEMENTS
80. MORE COMPLEX ALGORITHMS THAT INCLUDE OUTSIDE INFLUENCE
82. Lehman, Clune, & Misevic, 2018
83. Cheney, MacCurdy, Clune, Lipson, 2013
85. BE READY
86. DON’T CONFUSE THE MAP WITH THE TERRITORY
87. VERIFY AND CHECK SOLUTIONS DERIVED FROM SIMULATION
90. BUT WHAT HAPPENS WITH DIALECTAL LANGUAGE? Blodgett, Green, and O’Connor, 2016
91. MANY AI/ML TOOLS ARE TRAINED TO MINIMIZE AVERAGE LOSS
92. REPRESENTATION DISPARITY Hashimoto, Srivastava, Namkoong, and Liang, 2018
94. CONSIDER PREDICTIVE ACCURACY AS A RESOURCE TO BE ALLOCATED Hashimoto, Srivastava, Namkoong, and Liang, 2018
95. DISTRIBUTIONALLY ROBUST OPTIMIZATION Hashimoto, Srivastava, Namkoong, and Liang, 2018
97. LET’S BUILD A PRODUCT WITH OUR TWITTER NLP
98. WHAT HAPPENS TO PEOPLE WHO USE DIALECT?
99. PREDICTIVE POLICING
100. Image via Reddit, Author user u/jakeroot
101. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017
102. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017
103. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017
104. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017
105. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017
106. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017
108. IGNORE OR ADJUST FOR ALGORITHM-SUGGESTED RESULTS
109. LOOK TO CONTROL ENGINEERING
110. 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
112. CLASS I - PHANTOMS OF FALSE CORRELATION Know what question you’re asking Trust conditional probability over straight correlation
113. 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
114. 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
115. CLASS V – THE SIMULATION SURPRISE Don’t confuse the map with the territory Always reality-check solutions from simulations
116. 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
117. CLASS VII - THE FEEDBACK DEVIL Ignore or adjust for algorithm-suggested results Look to control engineering for potential answers
121. MODELS REPRESENT WHAT WAS THEY DON’T TELL YOU WHAT SHOULD BE
123. OR GET TRAINING
124. Bootcamps Coursera Udemy Actual Universities
126. AI Now Institute Georgetown Law Center on Privacy and Technology Knight Foundation’s AI ethics initiative fast.ai
127. ABIDE BY ETHICS GUIDELINES
128. Privacy / Consent Transparency of Use Transparency of Algorithms Ownership
129. https://www.accenture.com/_acnmedia/PDF-24/Accenture-Universal-Principles-Data-Ethics.pdf
130. Slides: Arthur Doler @arthurdoler arthurdoler@gmail.com Handout: None