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Machine learning how not to lose the user Slide 1 Machine learning how not to lose the user Slide 2 Machine learning how not to lose the user Slide 3 Machine learning how not to lose the user Slide 4 Machine learning how not to lose the user Slide 5 Machine learning how not to lose the user Slide 6 Machine learning how not to lose the user Slide 7 Machine learning how not to lose the user Slide 8 Machine learning how not to lose the user Slide 9 Machine learning how not to lose the user Slide 10 Machine learning how not to lose the user Slide 11 Machine learning how not to lose the user Slide 12 Machine learning how not to lose the user Slide 13 Machine learning how not to lose the user Slide 14 Machine learning how not to lose the user Slide 15 Machine learning how not to lose the user Slide 16 Machine learning how not to lose the user Slide 17 Machine learning how not to lose the user Slide 18 Machine learning how not to lose the user Slide 19 Machine learning how not to lose the user Slide 20 Machine learning how not to lose the user Slide 21 Machine learning how not to lose the user Slide 22 Machine learning how not to lose the user Slide 23 Machine learning how not to lose the user Slide 24 Machine learning how not to lose the user Slide 25 Machine learning how not to lose the user Slide 26 Machine learning how not to lose the user Slide 27 Machine learning how not to lose the user Slide 28 Machine learning how not to lose the user Slide 29 Machine learning how not to lose the user Slide 30 Machine learning how not to lose the user Slide 31 Machine learning how not to lose the user Slide 32 Machine learning how not to lose the user Slide 33 Machine learning how not to lose the user Slide 34
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Machine learning how not to lose the user

My Talk at UXI 2018 about designing products that include machine learning capabilities

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Machine learning how not to lose the user

  1. 1. @nuritps MACHINE LEARNING
  2. 2. 3 BERNARD PARKERDYLAN FUGGET • One attempted burglary • Three drug possessions • One resisting to arrest • No offenses Risk: 10vsRisk: 3
  3. 3. ML: High risk | Verdict: Not guilty ML: Low Risk | Verdict: Guilty 24% 45% White African American 48% 28% White American African
  4. 4. Data Collection Test https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
  5. 5. &
  6. 6. tweet #uxilive @nuritps
  7. 7. HOW DANGEROUS A PERSON IS
  8. 8. HOW DANGEROUS A PERSON IS Ranked as safe and is one Ranked as dangerous and is one Ranked as safe but isn’t Ranked as dangerous but isn’t RANKED DANGEROUS?
  9. 9. FREE even if GUILTY LOCK even if NOT GUILTY
  10. 10. tweet #uxilive @nuritps
  11. 11. Important according to our magic sauce
  12. 12. Advanced Settings If breaches are from related hosts
  13. 13. http://social.cs.uiuc.edu/papers/Alg orithms_Workshop_ICWSM.pdf
  14. 14. Confidence: Strong
  15. 15. Click to teach Gmail this conversation is important
  16. 16. tweet #uxilive @nuritps
  17. 17. tweet #uxilive @nuritps
  18. 18. Nurit Peres, 2018 | nuritps@gmail.com | @nuritps

My Talk at UXI 2018 about designing products that include machine learning capabilities

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