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

1. @nuritps
MACHINE LEARNING

7. 3
BERNARD PARKERDYLAN FUGGET
• One attempted burglary
• Three drug possessions
• One resisting to arrest
• No offenses
Risk: 10vsRisk: 3

8. ML: High risk | Verdict: Not guilty ML: Low Risk | Verdict: Guilty
24%
45%
White African American
48%
28%
White American African

9. Data Collection Test
https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

13. &

16. tweet
#uxilive
@nuritps

18. HOW DANGEROUS A PERSON IS

19. 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?

20. FREE even
if GUILTY
LOCK even
if NOT GUILTY

21. tweet
#uxilive
@nuritps

25. Important according to our magic sauce

26. Advanced Settings
If breaches are from related hosts

27. http://social.cs.uiuc.edu/papers/Alg
orithms_Workshop_ICWSM.pdf

29. Confidence: Strong

30. Click to teach Gmail this conversation is important

31. tweet
#uxilive
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33. tweet
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34. Nurit Peres, 2018 | nuritps@gmail.com | @nuritps