An AI with an Agenda: How Our Biases Leak Into Machine Learning (KCDC 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-kcdc2019 None
2. Titanium Sponsors Platinum Sponsors Gold Sponsors
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 IV is boring) Class V – The Simulation Surprise Class VI – Apparition of Fairness Class VII – The Feedback Devil
31. http://www.tylervigen.com/spurious-correlations - Data sources: Centers for Disease Control & Prevention and Internet Movie Database
32. http://www.tylervigen.com/spurious-correlations - Data sources: National Vital Statistics Reports and U.S. Department of Agriculture
33. http://www.tylervigen.com/spurious-correlations - Data sources: National Spelling Bee and Centers for Disease Control & Prevention
35. KNOW WHAT QUESTION YOU’RE ASKING UP FRONT
36. USE CONDITIONAL PROBABILITY OVER CORRELATION
37. https://versionone.vc/correlation-probability/
40. MORTGAGE LENDING ANALYSIS
48. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G
49. Twitter - @quantoidasaurus (Used with permission)
50. YOUR SAMPLE MIGHT NOT BE REPRESENTATIVE
51. YOUR DATA MIGHT NOT BE REPRESENTATIVE
53. MODELS REPRESENT WHAT WAS THEY DON’T TELL YOU WHAT SHOULD BE
54. FIND A BETTER DATA SET! CONCEPTNET.IO
55. BUILD A BETTER DATA SET!
57. BEWARE SHADOW COLUMNS
58. MAKE SURE YOUR SAMPLE SET IS REPRESENTATIVE
61. IBM’S AI FAIRNESS TOOLKIT
62. https://aif360.mybluemix.net AI FAIRNESS TOOLKIT
63. https://aif360.mybluemix.net
67. https://aif360.mybluemix.net
68. https://aif360.mybluemix.net
69. https://pair-code.github.io/what-if-tool
70. HAVE A GOOD PROCESS
71. KEEP IN MIND YOU NEED TO KNOW WHO CAN BE AFFECTED IN ORDER TO UN-BIAS
73. PRICING ALGORITHMS
74. Calvano, Calzolari, Denicolò and Pastorello (2018)
77. Calvano, Calzolari, Denicolò and Pastorello (2018)
78. WHAT IF AMAZON BUILT A SALARY TOOL INSTEAD?
79. THE BRATWURST PROBLEM
80. HUMANS ARE RARELY SINGLE-MINDED
83. https://www.alexirpan.com/2018/02/14/rl-hard.html; Gu, Lillicrap, Sutskever, & Levine, 2016
85. MODELS REPRESENT WHAT WAS THEY DON’T TELL YOU WHAT SHOULD BE
86. DON’T TRUST ALGORITHMS TO MAKE SUBTLE OR LARGE MULTI-VARIABLE JUDGEMENTS
88. MORE COMPLEX ALGORITHMS THAT INCLUDE OUTSIDE INFLUENCE
90. Lehman, Clune, & Misevic, 2018
91. Cheney, MacCurdy, Clune, Lipson, 2013
93. BE READY
94. DON’T CONFUSE THE MAP WITH THE TERRITORY
95. VERIFY AND CHECK SOLUTIONS DERIVED FROM SIMULATION
98. BUT WHAT HAPPENS WITH DIALECTAL LANGUAGE? Blodgett, Green, and O’Connor, 2016
99. MANY AI/ML TOOLS ARE TRAINED TO MINIMIZE AVERAGE LOSS
100. REPRESENTATION DISPARITY Hashimoto, Srivastava, Namkoong, and Liang, 2018
102. CONSIDER PREDICTIVE ACCURACY AS A RESOURCE TO BE ALLOCATED Hashimoto, Srivastava, Namkoong, and Liang, 2018
103. DISTRIBUTIONALLY ROBUST OPTIMIZATION Hashimoto, Srivastava, Namkoong, and Liang, 2018
105. LET’S BUILD A PRODUCT WITH OUR TWITTER NLP
106. WHAT HAPPENS TO PEOPLE WHO USE DIALECT?
107. PREDICTIVE POLICING
108. Image via Reddit, Author user u/jakeroot
109. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017
110. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017
111. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017
112. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017
113. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017
114. Ensign, Friedler, Neville, Scheidegger, & Venkatasubramanian, 2017
116. IGNORE OR ADJUST FOR ALGORITHM-SUGGESTED RESULTS
117. LOOK TO CONTROL ENGINEERING
118. 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
120. CLASS I - PHANTOMS OF FALSE CORRELATION Know what question you’re asking Trust conditional probability over straight correlation
121. 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
122. 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
123. CLASS V – THE SIMULATION SURPRISE Don’t confuse the map with the territory Always reality-check solutions from simulations
124. 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
125. CLASS VII - THE FEEDBACK DEVIL Ignore or adjust for algorithm-suggested results Look to control engineering for potential answers
128. MODELS REPRESENT WHAT WAS THEY DON’T TELL YOU WHAT SHOULD BE
130. OR GET TRAINING
131. Bootcamps Coursera Udemy Actual Universities
133. AI Now Institute Georgetown Law Center on Privacy and Technology Knight Foundation’s AI ethics initiative fast.ai Algorithmic Justice League
134. ABIDE BY ETHICS GUIDELINES
135. Privacy / Consent Transparency of Use Transparency of Algorithms Ownership
136. https://www.accenture.com/_acnmedia/PDF-24/Accenture-Universal-Principles-Data-Ethics.pdf
137. Slides: Arthur Doler @arthurdoler arthurdoler@gmail.com Handout: bit.ly/art-ai-agenda-kcdc2019 None