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Fairness Christos Dimitrakakis October 3, 2019 . . . . . . . - PowerPoint PPT Presentation

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Fairness Christos Dimitrakakis October 3, 2019 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 1 / 41 Fairness definitions

  1. Fairness Christos Dimitrakakis October 3, 2019 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 1 / 41

  2. Fairness definitions Fairness What is it? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 2 / 41

  3. Fairness definitions Fairness What is it? ▶ Meritocracy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 2 / 41

  4. Fairness definitions Fairness What is it? ▶ Meritocracy. ▶ Proportionality and representation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 2 / 41

  5. Fairness definitions Fairness What is it? ▶ Meritocracy. ▶ Proportionality and representation. ▶ Equal treatment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 2 / 41

  6. Fairness definitions Fairness What is it? ▶ Meritocracy. ▶ Proportionality and representation. ▶ Equal treatment. ▶ Non-discrimination. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 2 / 41

  7. Fairness definitions Meritocracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 3 / 41

  8. Fairness definitions Meritocracy Example 1 (College admissions) ▶ Student A has a grade 4/5 from Gota Highschool. ▶ Student B has a grade 5/5 from Vasa Highschool. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 3 / 41

  9. Fairness definitions Meritocracy Example 1 (College admissions) ▶ Student A has a grade 4/5 from Gota Highschool. ▶ Student B has a grade 5/5 from Vasa Highschool. Example 2 (Additional information) ▶ 70% of admitted Gota graduates with 4+ get their degree. ▶ 50% of admitted Vasa graduates with 5 get their degree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 3 / 41

  10. Fairness definitions Meritocracy Example 1 (College admissions) ▶ Student A has a grade 4/5 from Gota Highschool. ▶ Student B has a grade 5/5 from Vasa Highschool. Example 2 (Additional information) ▶ 70% of admitted Gota graduates with 4+ get their degree. ▶ 50% of admitted Vasa graduates with 5 get their degree. We still don’t know how a specific student will do! Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 3 / 41

  11. Fairness definitions Meritocracy Example 1 (College admissions) ▶ Student A has a grade 4/5 from Gota Highschool. ▶ Student B has a grade 5/5 from Vasa Highschool. Example 2 (Additional information) ▶ 70% of admitted Gota graduates with 4+ get their degree. ▶ 50% of admitted Vasa graduates with 5 get their degree. We still don’t know how a specific student will do! Solutions ▶ Admit everybody? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 3 / 41

  12. Fairness definitions Meritocracy Example 1 (College admissions) ▶ Student A has a grade 4/5 from Gota Highschool. ▶ Student B has a grade 5/5 from Vasa Highschool. Example 2 (Additional information) ▶ 70% of admitted Gota graduates with 4+ get their degree. ▶ 50% of admitted Vasa graduates with 5 get their degree. We still don’t know how a specific student will do! Solutions ▶ Admit everybody? ▶ Admit randomly? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 3 / 41

  13. Fairness definitions Meritocracy Example 1 (College admissions) ▶ Student A has a grade 4/5 from Gota Highschool. ▶ Student B has a grade 5/5 from Vasa Highschool. Example 2 (Additional information) ▶ 70% of admitted Gota graduates with 4+ get their degree. ▶ 50% of admitted Vasa graduates with 5 get their degree. We still don’t know how a specific student will do! Solutions ▶ Admit everybody? ▶ Admit randomly? ▶ Use prediction of individual academic performance? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 3 / 41

  14. Fairness definitions Proportional representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 4 / 41 https:

  15. Fairness definitions Hiring decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 5 / 41

  16. Fairness definitions Fairness and information Example 3 (College admissions data) School Male Female A 62% 82% B 63% 68% C 37% 34% D 33% 35% E 28% 24% F 6% 7% Average 45% 38% . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 6 / 41

  17. Fairness in machine learning Bail decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 7 / 41

  18. Fairness in machine learning Bail decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 7 / 41

  19. Fairness in machine learning Bail decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 7 / 41

  20. Fairness in machine learning Bail decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 7 / 41

  21. Fairness in machine learning Bail decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 7 / 41

  22. Fairness in machine learning Bail decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 7 / 41

  23. Fairness in machine learning Bail decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 7 / 41

  24. Fairness in machine learning Whites get lower scores than blacks 1 600 600 500 500 Count Count 400 400 300 300 200 200 100 100 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Risk Score Risk Score Black White Figure: Apparent bias in risk scores towards black versus white defendants. 1 Pro-publica, 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 8 / 41

  25. Fairness in machine learning But scores equally accurately predict recidivsm 2 Figure: Recidivism rates by risk score. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 9 / 41 2 Washington Post, 2016

  26. Fairness in machine learning But non-offending blacks get higher scores Figure: Score breakdown based on recidivism rates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 10 / 41

  27. Fairness in machine learning Graphical models and independence ▶ Why is it not possible to be fair in all respects? ▶ Different notions of conditional independence. ▶ Can only be satisfied rarely simultaneously. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 11 / 41

  28. Graphical models Graphical models x 2 x 1 x 3 Figure: Graphical model (directed acyclic graph) for three variables. Joint probability Let x = ( x 1 , . . . , x n ). Then x : Ω → X , X = ∏ i X i and: P ( x ∈ A ) = P ( { ω ∈ Ω | x ( ω ) ∈ A } ) . Factorisation P ( x ) = P ( x B | x C ) P ( x C ) , B , C ⊂ [ n ] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dimitrakakis Fairness October 3, 2019 12 / 41

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