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Machine learning how not to lose the user

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

Publicado en: Tecnología
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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

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