AI and Ethics - We are the guardians of our future

1. WE ARE THE GUARDIANS OF OUR FUTURE Tess Ferrandez Photo: Porapak Apichodilok
2. Photo: Rosemary Ketchum GANG CRIME CLASSIFICATION Partially Generative Neural Networks For Gang Crime Classification With Partial Information
3. Photo: Perry Wilson 42 BABIES 28 ADMITTED GANG MEMBERS
4. I’M JUST AN ENGINEER
5. ONCE THE ROCKETS ARE UP WHO CARES WHERE THEY COME DOWN? That is not my department said Werner von Braun Photo: Pixabay
6. I’M JUST AN ENGINEER
7. I’M NOT JUST AN ENGINEER
8. Photo: m01229
9. Photo: Canned Muffins AI GAYDAR PAPER Deep Neural Networks Can Detect Sexual Orientation from Faces
10. Photo: m01229 CRIMINAL FACES Automated Inference on Criminality Using Face Images
11. CRIMINALS VIOLENT NON-CRIMINALS GOVERNMENT IDS CORPORATE HEADSHOTS
12. CRIMINAL NON-CRIMINAL
13. 90%
14. 86%
15. CAN WE DO IT?
16. SHOULD WE DO IT?
17. WE ARE CURRENTLY IN THE BIGGEST EXPERIMENT OF CLASSIFICATION IN HUMAN HISTORY Kate Crawford
18. Photo: from cover of Neue Illustrierte Zeitung on June 1, 1933
19. Photo: Bartek Wojtas
20. RESPONSIBILITY LAUNDERING Photo: Bartek Wojtas
21. Photo: Anton Mislawsky ALGORITHMS CAN’T BE RACIST THEY’RE JUST MATH
22. Photo: Sam Galison
23. LABELED FACES IN THE WILD
24. 77% MALE
25. 80% WHITE
26. 5% GEORGE W BUSH
27. JOY BOULAMWINI GENDER SHADES
28. https://metro.co.uk/2018/07/06/weve-got-to-stop-the-met-polices-dangerously-authoritarian-facial-recognition-surveillance-7687833/
29. Photo: Anton Mislawsky ALGORITHMS CAN’T BE RACIST THEY’RE JUST MATH BIAS LAUNDERING
30. CONFIRMATION BUBBLE
31. Photo: Fadil Elmansour AREA W. HIGH CRIME SEND MORE POLICE DO MORE ARRESTS APPEARS TO HAVE MORE CRIME SEND MORE POLICE DO MORE ARRESTS RUNAWAY FEEDBACK LOOP
32. BIAS IS AN NP HARD PROBLEM
33. REMOVE GENDER EQUAL OUTCOME EQUAL OPPORTUNITY
34. SHOULD WE JUST GIVE UP?
35. FAIR AND INCLUSIVE TRANSPARENT ACCOUNTABLE SAFE AND RELIABLE SECURITY AND PRIVACY FAIR AND INCLUSIVE TRANSPARENT ACCOUNTABLE SAFE AND RELIABLE SECURITY AND PRIVACY
36. CONSIDER HOW THE TECHNOLOGY COULD BE USED Photo: Pixabay
37. DATASHEETS FOR DATASETS
38. EVALUATE FOR FAIRNESS
39. TRANSPARENT DECISIONSTRANSPARENT DECISIONS
40. DETECT VS PREDICT
41. COVERED FACES ALONE MEN 20-40 HOODIES SHOPLIFTING POSES MEN 20-40 HOODIESCOVERED FACES ALONE
42. Photo: George Becker DEALING WITH ERRORS
43. HUMAN IN THE LOOP
44. WE’RE NOT JUST ENGINEERS
45. WE ARE THE GUARDIANS OF OUR FUTURE Tess Ferrandez Photo: Porapak Apichodilok

Editor's Notes

23% no proof
Living on a certain block can label you as Crip or Hells Angel
Used to justify arrest, support maximum penalties
No right to know if you’re on it, or to challenge it
Used for background checks for jobs

Quote from a song by Tom Lehrer

The AI Gaydar paper

Wang, Y., & Kosinski, M. (2018). Deep neural networks are more accurate than humans at detecting sexual orientation from facial images

50 clicks on facebook
Used by Cambridge analytica

81% men, 74% women
Data from dating websites

Fluidity, what determines someones preferences? Bi? Bi-curious? outspoken

Long tail, criminal in k countries, death penalty,

Criminal or Not = 90%

1856 Images of Chinese Men 18-55, No facial hair or scars – faces cut out730 -- Criminals – ID From Police Department 235 Violent Crimes,
536 Theft, Fraud, Corruption, Forgery, Racketeering1126 -- Random images from internet – “corporate photos” as Non-Criminalsnote: 90%, SAME NETWORK ARCHITECTURE WAS ONLY ABLE TO PICK UP GENDER IN 86%

Criminals
Non-criminals

Criminal or Not = 90%

Gender w. AlexNet = 86%

Harm can further be classified into five types: stereotyping, recognition, denigration, under-representation and ex-nomination.

Classification is always a product of its time
We are currently in the biggest experiment of classification in human history

Kate Crawford

Wouldn’t be able to tell one Wookie from the next
Wife: Malla, Son: Lumpy

Areas with more crime get more policing => more crimes reported => considered worse neighborhood, more policing

Scrubbing to neutral
What is neutral – gender, ethnicity, are people from lapponia counted as finnish, sexual orientation, political orientation?
What about words like pregnancy, or color blindness? Queen?
Who decides?
Everyone is a majority somewhere and a minority somewhere else

Quote from a song by Tom Lehrer

AI and Ethics - We are the guardians of our future

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AI and Ethics - We are the guardians of our future

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