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On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness & Feature Segregation via Social Learning Hoda Heidari, Vedant Nanda, Krishna Gummadi Automated Decision Making Algorithmic Unfairness Problems with Static Notions


  1. On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness & Feature Segregation via Social Learning Hoda Heidari, Vedant Nanda, Krishna Gummadi

  2. Automated Decision Making

  3. Algorithmic Unfairness

  4. Problems with Static Notions of Fairness

  5. Problems with Static Notions of Fairness

  6. Problems with Static Notions of Fairness

  7. Problems with Static Notions of Fairness

  8. Effort Unfairness ● Economic models of Equality of Opportunity (Roemer, 2002) ● For an individual in group s ● changing a monotone feature from x to x’ ● takes effort max { 0, Q s (x') - Q s (x) }.

  9. Long-term Impact ● Our proposal: ○ People will exert effort and invest in qualifications ○ that improve their utility (= reward - effort) . ● Individual-level model: social learning (Bandura, 1962) ○ Individuals imitate the behavior of their social models . ○ Social model is another decision subject imitating whom maximizes utility ● Population-level measure: segregation

  10. Measuring Impact on Qualifications

  11. Fairness Interventions

  12. Food for Thought ● What is the ultimate purpose of a fair model? ● Are all errors created equal? ● Is it ethically acceptable ○ To incentivize different groups to obtain different qualification? ○ To employ an “unfair” model today to guarantee long-term equality? ● ...

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