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The Ethics of Everybody Else

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We aren’t surprised by facial recognition at security checkpoints. But how do you feel about face-scanning toilet roll dispensers? What if they don’t just find criminals but try to detect “criminality”? Laws and policies almost always lag technology so data scientists and machine learning experts are among the first line of ethical defense. The argument in this talk is that to be ethical, any system that classifies human beings has to consider the goals of the people affected by the system, not just the builders’ goals. This is not particularly convenient, but there are concrete ways to put goal-oriented design into practice. Doing so puts us in a better position to practice ethical behavior and attempt to address problems of power and the reproduction of inequality.

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The Ethics of Everybody Else

  1. 1. T H E E T H I C S O F E V E RY B O D Y E L S E T Y L E R S C H N O E B E L E N , I N T E G R AT E . A I
  2. 2. S O L E T ’ S K I C K S O M E S H I T M Y D A D C A L L S T H E S E S H I T K I C K E R S
  3. 3. I R E A L LY R E A L LY D O N ’ T L I K E “ S H I T ”
  4. 4. S A N G T H E N I G H T M A R E T O I L E T R O L L D I S P E N S E R O F M Y J A PA N E S E H O S T FA M I LY “It’s a small world after all…”
  5. 5. – R E P R E S E N TAT I V E S T E V E K I N G ( R - M Y H O M E S TAT E ) “We can't restore our civilization with somebody else's babies.”
  6. 6. O T H E R I N G I N - G R O U P S G E T T O B E H E T E R O G E N O U S I N D I V I D U A L S , F O R E V E RY O N E E L S E T H E R E ’ S
  7. 7. T H E C O R E C L A I M Data scientists and AI practitioners must consider the goals of the people affected by the systems they design and build
  8. 8. B A S I C O U T L I N E • 3 kinds of problems • An easy unethical project • Training data, ethical frameworks, and categories • What you think of people • Practical recommendations • Technology doesn’t just happen
  9. 9. A T Y P O L O G Y O F P R O B L E M S ( R I T T E L A N D W E B B E R , 1 9 7 3 ) • Simple problems: Identify stakeholders, articulate their goals, build a plan, execute • Complex problems: Decompose into multiple simple problems • But some problems are…
  10. 10. A T Y P O L O G Y O F P R O B L E M S ( R I T T E L A N D W E B B E R , 1 9 7 3 ) • Simple problems: Identify stakeholders, articulate their goals, build a plan, execute • Complex problems: Decompose into multiple simple problems • Wicked problems: You can articulate goals but they are fundamentally in conflict. There is no definitive solution.
  11. 11. A N E A S Y U N E T H I C A L P R O J E C T D E T E C T “ C R I M I N A L I T Y ” ( B U I L D E R G O A L ~ P U B L I C S A F E T Y )
  12. 12. All four classifiers perform consistently well and produce evidence for the validity of automated face-induced inference on criminality… Also, we find some discriminating structural features for predicting criminality, such as lip curvature, eye inner corner distance, and the so-called nose- mouth angle.
  13. 13. G E T Y O U R FA C E S C A N N E D F O R 7 0 C M O F T O I L E T PA P E R
  14. 14. P E R C E N TA G E O F M O D E L S W I T H N O FA L S E P O S I T I V E S ~ 0 %
  15. 15. O K AY, FA C I A L R E C O G N I T I O N I S D O I N G W E L L FA C T C H E C K
  16. 16. O N E Y E A R A F T E R T H O S E S TAT S A LT H O U G H Y O U M AY R E M E M B E R …
  17. 17. P L A C E Y O U R T R U S T I N B I A S H T T P S : / / O P E N P O L I C I N G . S TA N F O R D . E D U / F I N D I N G S / ( P I E R S O N E T A L , 2 0 1 7 )
  18. 18. 6 0 % O F S T O P S W E R E O F A F R I C A N A M E R I C A N S , W H O M A K E U P 2 8 % O F O A K L A N D ’ S P O P U L AT I O N I N O A K L A N D ( E B E R H A R D T E T A L , 2 0 1 6 )
  19. 19. T H E I N T E R A C T I O N S T H E M S E LV E S H AV E D I F F E R E N T Q U A L I T I E S L O G - O D D S R AT I O S F O R O F F I C E R S P E E C H I N O A K L A N D
  20. 20. A N D V O I C E S A R E I G N O R E D S E E R I C K F O R D & K I N G ( 2 0 1 6 ) O N H O W R A C H E L J E A N T E L’ S T E S T I M O N Y WA S D I S C O U N T E D
  21. 21. Three ethical frameworks
  22. 22. V I R T U E E T H I C S : T H E A C T O R ' S M O R A L C H A R A C T E R A N D D I S P O S I T I O N S E E , F O R E X A M P L E , A N N A S 1 9 9 8
  23. 23. D E O N T O L O G Y: T H E D U T I E S A N D O B L I G AT I O N S O F T H E A C T O R G I V E N T H E I R R O L E S E E , F O R E X A M P L E , K A M M 2 0 0 8
  24. 24. C O N S E Q U E N T I A L I S M : I T ’ S T H E O U T C O M E S O F T H E A C T I O N S ( U T I L I TA R I A N I S M I S T H E M O S T FA M O U S V E R S I O N O F T H I S — D O T H E M O S T G O O D F O R T H E M O S T P E O P L E ) S E E , F O R E X A M P L E , F O O T 1 9 6 7 ; TA U R E K 1 9 7 7 ; PA R F I T 1 9 7 8 ; T H O M S O N 1 9 8 5
  25. 25. T H E C O R E C L A I M Regardless of your preferred ethical framework, Data scientists and AI practitioners must consider the goals of the people affected by the systems they design and build
  26. 26. P E O P L E H AV E I M P L I C I T B I A S E S ( A N D T H E S E A R E F O U N D I N D ATA , C A L I S K A N E T A L 2 0 1 7 ) T RY O U T H T T P S : / / I M P L I C I T. H A R VA R D . E D U / I M P L I C I T / TA K E AT E S T. H T M L
  27. 27. Y O U R C AT E G O R I E S A R E W R O N G ( T H E Y M AY B E U S E F U L )
  28. 28. C O N S I D E R X O A C R O S S 1 4 K T W I T T E R U S E R S • A lot more women use xo than men • 11% of all women • 2.5% of all men • But that means that 89% of women aren’t using it at all. • People who use xo are three times more likely to use ttyl (‘talk to you later’) • The style is more commonly adopted by women • But there’s other stuff going on here: age, job, etc. • It’s not clear that gender is even the most important, it’s just that we’re starting with gender-colored glasses
  29. 29. P E O P L E A R E N O T J U S T T H E S U M O F D I F F E R E N T D E M O G R A P H I C C H A R A C T E R I S T I C S I N T E R S E C T I O N A L I T Y ( C R E N S H A W, 1 9 8 9 )
  30. 30. D O Y O U T H I N K P E O P L E A R E S TAT I C ? 
 F O R Y O U , A R E T H E Y I N H E R E N T LY G O O D O R B A D ? M O S T R E S E A R C H S U G G E S T S T H AT G O O D N E S S I S C O N T E X T U A L
  31. 31. T H E O L O G Y S T U D E N T S I N A R U S H T O G I V E A TA L K D O N O T H E L P A S T R A N G E R I N N E E D E V E N W H E N T H E TA L K T H E Y A R E H U R RY I N G T O G I V E I S A B O U T T H E G O O D S A M A R I TA N D A R L E Y A N D B AT S O N ( 1 9 7 3 )
  32. 32. W E S E E M C O N S I S T E N T B E C A U S E W E T E N D T O B E I N C O N S I S T E N T S I T U AT I O N S / R E L AT I O N S H I P S T O E A C H O T H E R T H E S TAT U S Q U O M A I N TA I N S I T S E L F B E C A U S E W E T E N D T O D O T H E T H I N G W E D I D B E F O R E F O R S O C I A L T H E O RY A L O N G T H E S E L I N E S , S E E B O U R D I E U , 1 9 7 7 ; G I D D E N S , 1 9 8 4 ; B U T L E R , 1 9 9 9
  33. 33. - J A M E S S C O T T ( 1 9 9 0 ) “Power means not having to act, or more accurately, the capacity to be more negligent and casual about any single performance” Systems are not equally hospitable to all people They require some people to perform acrobatics and contortions to get by
  34. 34. S O M E P R A C T I C A L T H I N G S T O D O
  35. 35. 1 ) D O A P R E M O R T E M H AV E T H E T E A M W R I T E O U T W H AT W E N T W R O N G … B E F O R E T H E P R O J E C T E V E N B E G I N S ( K L E I N 2 0 0 7 )
  36. 36. 2 ) L I S T P E O P L E A F F E C T E D A N D Y O U N E E D T O TA L K T O T H E M
  37. 37. A F F E C T E D M E A N S A F F E C T E D I N T H E I R O W N T E R M S For example, Jehovah’s Witnesses refuse blood transfusions You could choose to ignore what someone says matters to them…but when, where, why, and with whom?
  38. 38. 3 ) D E T E R M I N E I F I T ’ S A W M D • Opaque to the people they affect • Affect important aspects of life • Education • Housing • Health • Work • Justice • Finance/credit • Can do real damage
  39. 39. 4 ) A S K F O R J U S T I F I C AT I O N S • Go on Ethical High Alert when you hear: • Everyone else is doing it and we have to keep up • No one else is doing it so we can lead the pack • It makes money • It's legal • It's inevitable • Check out Pope & Vasquez (2016) and https://kspope.com/ethics/ ethicalstandards.php
  40. 40. 5 ) N A M E T H E VA L U E S E N S H R I N E D ( A N D T H E O N E S AT O D D S ) W H AT * A R E * Y O U R VA L U E S ?
  41. 41. It’s not a principle until it costs you something.
  42. 42. 6 ) C O N S I D E R D E F E N S I V E E T H I C A L P O S I T I O N I N G ( W O R K S B E T T E R I N I N D I A A N D T H E U S T H A N I N A U S T R A L I A , D E S A I & K O U C H A K I 2 0 1 7 )
  43. 43. I F Y O U ’ R E I N T H I S R O O M , Y O U C A N P R O B A B LY W R I T E Y O U R O W N T I C K E T A N D H E L P O T H E R S S E E T H AT T H E Y C A N , T O O B T W, W H AT D O Y O U WA N T T O B E D O I N G ? ( P S - W E ’ R E H I R I N G )
  44. 44. – J A C K M A , F O U N D E R / E X E C C H A I R M A N O F A L I B A B A “The first technology revolution caused World War I”
  45. 45. ~ B R E A K D O W N O F T H E C O N G R E S S O F V I E N N A M O R E L I K E I M P E R I A L I S T P O L I T I C S C O M I N G H O M E T O R O O S T
  46. 46. A H I S T O RY P R O F E S S O R R E S P O N D S “It also sort of annoys me because it ignores politics and actual decisions. People decide to go to war.” “We can decide not to go to war.”
  47. 47. T H E C O R E C L A I M Technology does not just happen Data scientists and AI practitioners must consider the goals of the people affected by the systems they design and build
  48. 48. I L O V E A G O O D K U M B AYA
  49. 49. C A L L I N G F O R H E L P F O R P E O P L E I N N E E D B U T I N R E A L I T Y K U M B AYA I S A S P I R I T U A L T H AT I S
  50. 50. I don’t worry about the ethics of how people treat AI’s
  51. 51. I don’t worry about how AI’s treat people
  52. 52. I worry about how people treat people
  53. 53. S C O U R G E D F R O M H E AV E N A N D H E L L W I L L N O T A C C E P T T H E M And I worry about being among The Uncommitted
  54. 54. I F W E T R A C E S H I T T O I T S R O O T S W E F I N D * S K E I ‘ T O C U T, S P L I T, D I V I D E , S E PA R AT E ’
  55. 55. W H E R E D O E S T H I S L E AV E U S ? • We can’t actually do our jobs or live our lives without making distinctions • We can recognize that distinctions have consequences • We can practice more care and questioning in our cutting • But…
  56. 56. T H E R E I S S T I L L A W O R L D O F O T H E R P E O P L E O U T S I D E O F T H I S R O O M • We need to take seriously Kate Crawford’s critique • Most of the people who build technology come from privileged backgrounds • This makes it difficult for our imagination and empathy to extend out to everyone our systems will affect • The implication is that we need NOT ONLY to attend to issues of diversity and representation • AND to educate communities who will be affected so that they, too, can voice their goals and values
  57. 57. T H E E X T E N S I O N O F T H E C O R E C L A I M Data scientists and AI practitioners must consider the goals of the people affected by the systems they design and build The practice of ethical design among experts leads to greater ethical capacity But ethics are too important to be left only to experts

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