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!

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”
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
19. SIX CLASSES OF PROBLEM WITH AI/ML
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
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
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
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!
58. BEWARE SHADOW COLUMNS
59. MAKE SURE YOUR SAMPLE SET IS REPRESENTATIVE
62. IBM’S AI FAIRNESS TOOLKIT
63. https://aif360.mybluemix.net AI FAIRNESS TOOLKIT
64. https://aif360.mybluemix.net
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
74. PRICING ALGORITHMS
75. Calvano, Calzolari, Denicolò and Pastorello (2018)
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
84. https://www.alexirpan.com/2018/02/14/rl-hard.html; Gu, Lillicrap, Sutskever, & Levine, 2016
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
89. MORE COMPLEX ALGORITHMS THAT INCLUDE OUTSIDE INFLUENCE
91. Lehman, Clune, & Misevic, 2018
92. Cheney, MacCurdy, Clune, & Lipson, 2013
93. Ellefsen, Mouret, & Clune, 2015 Lehman, Clune, & Misevic, 2018
95. BE READY
96. DON’T CONFUSE THE MAP WITH THE TERRITORY
97. VERIFY AND CHECK SOLUTIONS DERIVED FROM SIMULATION
100. BUT WHAT HAPPENS WITH DIALECTAL LANGUAGE? Blodgett, Green, and O’Connor, 2016
101. MANY AI/ML TOOLS ARE TRAINED TO MINIMIZE AVERAGE LOSS
103. REPRESENTATION DISPARITY Hashimoto, Srivastava, Namkoong, and Liang, 2018
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
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
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
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
131. MODELS REPRESENT WHAT WAS THEY DON’T TELL YOU WHAT SHOULD BE
133. OR GET TRAINING
134. Bootcamps Coursera Udemy Actual Universities
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
141. Slides: Arthur Doler @arthurdoler arthurdoler@gmail.com Handout: bit.ly/art-ai-agenda-necode2019 None