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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We're in charge, as data scientist and software engineers, of software that where mistakes have a very high price. It's time to look at the impact we have, how our software will be used and how to avoid creating unfair, biased and dangerous software
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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
- 6. I’M JUST AN ENGINEER
- 7. ONCE THE ROCKETS ARE UP WHO CARES WHERE THEY COME DOWN? That is not my department said Werner von Braun Photo: Pixabay
- 8. I’M JUST AN ENGINEER
- 9. I’M NOT JUST AN ENGINEER
- 10. Photo: m01229
- 13. Photo: Canned Muffins AI GAYDAR PAPER Deep Neural Networks Can Detect Sexual Orientation from Faces
- 15. Photo: m01229 CRIMINAL FACES Automated Inference on Criminality Using Face Images
- 20. 90%
- 21. 86%
- 22. CAN WE DO IT?
- 23. SHOULD WE DO IT?
- 24. WE ARE CURRENTLY IN THE BIGGEST EXPERIMENT OF CLASSIFICATION IN HUMAN HISTORY Kate Crawford
- 25. Photo: from cover of Neue Illustrierte Zeitung on June 1, 1933
- 30. Photo: Anton Mislawsky ALGORITHMS CAN’T BE RACIST THEY’RE JUST MATH
- 35. 77% MALE
- 36. 80% WHITE
- 37. 5% GEORGE W BUSH
- 43. Photo: Anton Mislawsky ALGORITHMS CAN’T BE RACIST THEY’RE JUST MATH BIAS LAUNDERING
- 50. 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
- 53. BIAS IS AN NP HARD PROBLEM
- 54. REMOVE GENDER EQUAL OUTCOME EQUAL OPPORTUNITY
- 55. SHOULD WE JUST GIVE UP?
- 56. FAIR AND INCLUSIVE TRANSPARENT ACCOUNTABLE SAFE AND RELIABLE SECURITY AND PRIVACY FAIR AND INCLUSIVE TRANSPARENT ACCOUNTABLE SAFE AND RELIABLE SECURITY AND PRIVACY
- 57. CONSIDER HOW THE TECHNOLOGY COULD BE USED Photo: Pixabay
- 64. COVERED FACES ALONE MEN 20-40 HOODIES SHOPLIFTING POSES MEN 20-40 HOODIESCOVERED FACES ALONE
- 67. Photo: George Becker DEALING WITH ERRORS
- 70. WE’RE NOT JUST ENGINEERS
- 71. 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 out 730 -- Criminals – ID From Police Department 235 Violent Crimes, 536 Theft, Fraud, Corruption, Forgery, Racketeering 1126 -- Random images from internet – “corporate photos” as Non-Criminals note: 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