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2019 WIA - The Importance of Ethics in Data Science

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2019 WIA - The Importance of Ethics in Data Science

  1. 1. Sustainable Machine Learningwww.alectio.com The Importance of Ethics in Data Science MAKING AI TEAMS WORK IN THE REAL WORLD Jennifer Prendki, PhD Founder & CEO, Alectio WIA Conference, Columbus, OH March 2019
  2. 2. Jennifer Prendki, PhD Founder and CEO, Alectio More about me: • Currently Expert Network @ IIA • Previously VP of Machine Learning @ Figure Eight, Chief Data Scientist @ Atlassian • Managed Applied Data Science Research in the Search team @ Walmart Labs • Have built & scaled ML functions in companies of all sizes
  3. 3. ALECTIO’S MISSION: Sustainable Machine Learning Helping Machine Learning teams build Machine Learning models with less resources (starting with less data)
  4. 4. AGENDA • Data: The New Oil? • Fatally Unprepared? • Data At All Costs? • Insane(ly Good) Machine Learning • Responsible Data Science ETHICS IN DATA SCIENCE AND MACHINE LEARNING
  5. 5. Data: The New Oil? WHY WE DATA SCIENTISTS LOVE OUR DATA…
  6. 6. An Explosion of Data
  7. 7. An Explosion of Data 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 50000 45000 40000 35000 30000 25000 20000 15000 10000 5000 0 Digital Data Growth Exabytes
  8. 8. An Explosion of Data 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 50000 45000 40000 35000 30000 25000 20000 15000 10000 5000 0 Digital Data Growth 1 Minute on the Internet… Exabytes
  9. 9. Big Data and Machine Learning Computer vision “invented”
  10. 10. Big Data and Machine Learning Computer vision “invented” ImageNet Classification Error (Top 5) 26.0 16.4 11.7 7.3 6.7 5.0 3.6 3.1 30.0 25.0 20.0 15.0 10.0 5.0 0.0 2011 (XRCE) 2012 (AlexNet) 2013 (ZF) 2014 (VGG) Huma n 2015 (ResNet) 2016 (GoogleNet- v4) 2014 (GoogleNet )
  11. 11. Big Data and Machine Learning • 14,000,000 labeled images • 20,000 categories Computer vision “invented” ImageNet Classification Error (Top 5) 16.4 11.7 7.3 6.7 5.0 3.6 3.1 30.0 25.0 20.0 15.0 10.0 5.0 0.0 2011 (XRCE) 2012 (AlexNet) 2013 (ZF) 2014 (VGG) 2014 (GoogleNet ) Huma n 2015 (ResNet) 2016 (GoogleNet- v4) 26.0
  12. 12. “ Data is Tech’s New Drug. ”
  13. 13. “ Data is Tech’s New Drug. ” “ Data is The New Plastic. ”
  14. 14. Fatally Unprepared? THE IMPACT OF THE BIG DATA ECONOMY ON SOCIETY
  15. 15. Socially fit? 100% 48% 85% 27% 94% 38% 46% 100% 82% 26%
  16. 16. Are you popular with A.I.? AndreyPopov|iStock|GettyImages
  17. 17. A.I. watching your every move… AndreyPopov|iStock|GettyImages
  18. 18. The Future Has Arrived Today ”Meet The Robinsons” | Disney®
  19. 19. The Future Has Arrived Today ”Meet The Robinsons” | Disney®
  20. 20. Progress… or Global Societal Abuse? Disappearance of Privacy
  21. 21. Progress… or Global Societal Abuse? Disappearance of Privacy Abuses of the Data Economy $
  22. 22. Progress… or Global Societal Abuse? Disappearance of Privacy Automation of Unfairness Abuses of the Data Economy $
  23. 23. Progress… or Global Societal Abuse? Disappearance of Privacy Automation of Unfairness Abuses of the Data Economy Malevolent Applications $
  24. 24. Data At All Costs? THE IMPACT OF THE BIG DATA ECONOMY ON SOCIETY
  25. 25. Datafication: a modern technological trend turning many aspects of our lives into data which is subsequently transferred into information realized as a new form of value.
  26. 26. Google Trends GDPR Data Protection Officer Privacy Ethics
  27. 27. A Brief History of Data Privacy Google Street View Behavior targeting is targeted Facebook Apps harvesting data w/out consent Voicemail Hacking Facebook & Cambridge Analytica GDPR EU Treaty went into effect Creation of the European Data Protection Directive Privacy in the News Proposal of GDPR Released Adoption by the EU Parliament GDPR valid
  28. 28. Data Labeling and the Gig Economy The human side of A.I.
  29. 29. Data Labeling and the Gig Economy The human side of A.I. o A dependency on human labor  Good side: communities
  30. 30. Data Labeling and the Gig Economy The human side of A.I. o A dependency on human labor  Good side: communities o A challenge for the workers  A tougher job than it might seem…
  31. 31. Data Labeling and the Gig Economy The human side of A.I. o A dependency on human labor  Good side: communities o A challenge for the workers  A tougher job than it might seem…
  32. 32. Data Labeling and the Gig Economy The human side of A.I. o A dependency on human labor  Good side: communities o A challenge for the workers  A tougher job than it might seem…
  33. 33. Data Labeling and the Gig Economy The human side of A.I. o A dependency on human labor  Good side: communities o A challenge for the workers  A tougher job than it might seem…
  34. 34. Data Labeling and the Gig Economy The human side of A.I. o A dependency on human labor  Good side: communities o A challenge for the workers  A tougher job than it might seem…
  35. 35. Data Labeling and the Gig Economy The human side of A.I. o A dependency on human labor  Good side: communities o A challenge for the workers  A tougher job than it might seem…  Slow human <> job matching  Overall
  36. 36. Data Labeling and the Gig Economy The human side of A.I. o A dependency on human labor  Good side: communities o A challenge for the workers  A tougher job than it might seem…  Slow human <> job matching  Overall o Inconsistent qualify of work  Error-prone tasks  Subjective tasks
  37. 37. Insane(ly good) Machine Learning? THE DR. JEKYLL AND MR. HIDE OF THE TECH WORLD
  38. 38. The Dark Side of Machine Learning June 2015: Google labels a black woman as a gorilla
  39. 39. Biases All Over the Place…
  40. 40. Biases All Over the Place… DATA BIAS o Labeling Bias  Subjective Labeling Tasks o Subgroup Validity  Simpson’s Paradox o Representation  Inappropriate Sampling Strategy
  41. 41. Biases All Over the Place… DATA BIAS ALGORITHMIC BIAS o Labeling Bias  Subjective Labeling Tasks o Subgroup Validity  Simpson’s Paradox o Representation  Inappropriate Sampling Strategy o Involuntary  Statistical Stereotyping o Voluntary  Agenda-Based
  42. 42. “ Data is the Reflection of our Society. ”
  43. 43. “ Data is the Reflection of our Society. ” … Machine Learning cannot fabricate Objectivity
  44. 44. License to Discriminate?
  45. 45. License to Discriminate?
  46. 46. License to Discriminate?
  47. 47. Explainability & Transparency
  48. 48. Responsible Data Science THE FUTURE OF DATA IS SPELLED E-T-H-I-C-S
  49. 49. A Scary World Ahead?
  50. 50. A Fairer AI Economy o General Patterns > Granular Insights
  51. 51. A Fairer AI Economy o General Patterns > Granular Insights o Social Impact > Feasibility
  52. 52. A Fairer AI Economy o General Patterns > Granular Insights o Social Impact > Feasibility o Human + Machine Collaboration > Competition
  53. 53. A Fairer AI Economy o General Patterns > Granular Insights o Social Impact > Feasibility o Human + Machine Collaboration > Competition o Ethics by Design > Legislation
  54. 54. Fairness vs. Biases • With ML, biases are of the essence… and that’s a good thing! • (Yes, you read that right!)
  55. 55. Fairness vs. Biases • With ML, biases are of the essence… and that’s a good thing! • (Yes, you read that right!) • Fairness is not ingrained in Machine Learning • Machines learn what we humans teach them • (Yes, even in the case of Reinforcement Learning)
  56. 56. Fairness vs. Biases • With ML, biases are of the essence… and that’s a good thing! • (Yes, you read that right!) • Fairness is not ingrained in Machine Learning • Machines learn what we humans teach them • (Yes, even in the case of Reinforcement Learning) Unfairness ≠ Bias ML is born of biases, but its societal purpose dies with unfairness
  57. 57. Responsible A.I. o Ethical o Inclusive (not exclusive to a privileged group) o No harm to society (no weaponization) o Centered on the well-being of Society
  58. 58. Be the Change you want to see in the World o Machine Learning will not become fair on its own  ML algorithms are by-products of human-generated data o Society and politicians are not ready  Uneducated users  No appropriate legislation in place o The one true prevention of unethical use of data is the Data Community CongresshearingofMarkZuckerberginApril 2018

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