Does this Algorithm have a Soul? - PyData Miami 2019

“Man is condemned to be free”, said Sartre. We as human beings are doomed and blessed by the power of choice, and some times, our choices may contain prejudice . What if we could pass this responsibility to a machine free of bias? Would we create a fair world our just a monster of our own image?

Vídeo: https://youtu.be/yNcUPORvhv4

Transcript

1. D O E S T H I S A L G O R I T H M
   H AV E A S O U L ?
   B E T I N A C O S TA
   

   View Slide

2. Consultant at ThoughtWorks
   AfroPython Volunteer
   Data Science Student
   Introverted Movie Buff
   

   View Slide

3. I N D U S T R I A L R E V O L U T I O N
   

   View Slide

4. M A C H I N E L E A R N I N G
   

   View Slide

5. O P E R AT I O N
   S E R E N ATA D E A M O R
   S I N G A P O R E
   H E A LT H C A R E
   

   View Slide

6. C A N W E E N D B I A S T H R O U G H
   D ATA ?
   

   View Slide

7. P R E J U D I C E
   

   View Slide

8. B U T I S T H AT T R U E ?
   

   View Slide

9. I N C O U RT
   

   View Slide

10. P R O P U B L I C A S E A R C H
    

    View Slide

11. B U T W H Y T H AT H A P P E N E D ?
    

    View Slide

12. • How many of your friends/acquaintances are taking
    drugs illegally ?
    • Was one of your parents ever sent to jail or prison?
    • How often did you get in fights at school?
    

    View Slide

13. C R I M E P R E D I C T I N G A L G O R I T H M S
    

    View Slide

14. F E E D B A C K L O O P
    

    View Slide

15. B R I N G I N G C L O S E R T O H O M E
    

    View Slide

16. FA C I A L R E C O G N I T I O N B I A S
    

    View Slide

17. S E A R C H E N G I N E S
    

    View Slide

18. S T O P M A C H I N E L E A R N I N G ?
    

    View Slide

19. D O N ’ T PA N I C
    

    View Slide

20. 1 . A C K N O W L E D G E T H E
    P R O B L E M
    

    View Slide

21. 2 . S P R E A D T H E
    P R O B L E M
    

    View Slide

22. 3 . N O B L A C K B O X E S
    

    View Slide

23. 4 . C H A N G E T H E S TAT U S
    Q U O
    

    View Slide

24. 5 . B E B E T T E R
    

    View Slide

25. – C AT H Y O ' N E I L
    “Algorithms are opinions embedded in code”
    

    View Slide

26. T H A N K Y O U !
    

    View Slide

27. R E F E R E N C E S
    • Racial Bias and Gender Bias Examples in AI Systems
    • Machine Bias
    • When an algorithm helps send you to prison
    • Artificial intelligence is now used to predict crime. But is it biased?
    • Runaway feedback loops in predictive policing
    • Assessing bias in search engines
    • The Hidden Gender bias in google image searches
    • Unequal representation and gender stereotypes in image search results for
    occupations
    • Algorithms of Oppression - Safiya Umoja Noble
    • Weapons of Math Destruction - Cathy O’Neil
    

    View Slide