This presentation was provided by Jason Griffey of NISO, during the virtual Computers in Libraries and Internet Librarian conference, held September 21-25, 2020.

1. AI Possible Futures!
Jason Griffey
Director of Strategic Initiatives
NISO
or
There is No Shortage of Terrible Ideas

2. Introduction

3. Introduction

4. Introduction

5. Introduction

6. Introduction

8. Introduction

10. Description of Session (not mine)
Should face recognition change the
way we interact with our customers?

11. Description of Session (not mine)
What if, for example, I can greet a person by
using their last name as soon as he/she gets
to the lobby because I have an iPad that
immediately shows me the customers
name, reservation, or even current fees?

12. Facial Recognition
is broken and racist

13. Libraries should not invest in
facial recognition systems

14. Libraries should not cooperate
with systems or services that use
facial recognition systems

15. Libraries should actively oppose
facial recognition systems
in their communities

16. State of
Artificial
Intelligence

17. StrongWeak

18. Photo by Clearly Ambiguous - http://flic.kr/p/31nkY

20. Biometrics?

21. Medical Diagnosis
Photo by Our Dream Photography (Personal) - http://flic.kr/p/RKYPaA

22. –Geoffrey Hinton, University of Toronto
“I think that if you work as a radiologist you are like
Wile E. Coyote in the cartoon. You’re already over the
edge of the cliff, but you haven’t yet looked down.
There’s no ground underneath. It’s just completely
obvious that in five years deep learning is going to do
better than radiologists. It might be ten years.”

25. https://www.microsoft.com/en-us/ai/seeing-ai

26. https://hamlet.andromedayelton.com

27. Photo by nathanmac87 - http://flic.kr/p/cx2Hvd

29. Photo by Mocks 108 - http://flic.kr/p/dEFhLT

32. Facial Recognition
is broken and racist

33. https://www.nytimes.com/2019/04/25/
lens/sarah-lewis-racial-bias-
photography.html

35. https://www.wired.com/story/best-algorithms-struggle-recognize-black-faces-equally/

36. https://www.nist.gov/news-events/news/2019/12/nist-study-evaluates-effects-race-age-sex-face-recognition-software

37. https://www.theatlantic.com/technology/archive/2016/04/the-underlying-bias-of-
facial-recognition-systems/476991/

38. Two People of Note

39. https://www.poetofcode.com

40. http://gendershades.org

41. https://www.ajl.org

42. https://www.aclu.org/news/privacy-technology/how-is-face-recognition-surveillance-technology-racist/

43. https://www.tedxcambridge.com/talk/why-face-surveillance-must-be-banned/

44. Outcomes & Examples

45. https://www.theverge.com/2020/8/26/21402978/clearview-ai-ceo-interview-2400-police-agencies-facial-recognition

46. https://www.nytimes.com/2020/06/24/technology/facial-recognition-arrest.html

47. https://venturebeat.com/2020/06/24/boston-bans-facial-recognition-due-to-concern-about-racial-bias/

48. https://www.ibm.com/blogs/policy/facial-recognition-sunset-racial-justice-reforms/

49. https://privacysos.org/blog/bias-all-the-way-down-research-shows-domino-effect-when-humans-use-face-recognition-algorithms/

50. https://twitter.com/futureesq1990/
status/1306393725099278336
https://twitter.com/uhreeb/status/
1303139738065481728

51. https://twitter.com/colinmadland/status/1307111816250748933

52. https://twitter.com/bascule/status/1307440596668182528

53. http://wfuogb.com/2020/08/zsr-pens-letter-against-proctoring-services/

54. Everything
is Fine

56. 82% White :: 5% Black :: 3% Latinx
http://www.ala.org/tools/research/librarystaffstats/diversity/libdirectors
http://www.ala.org/aboutala/sites/ala.org.aboutala/files/content/diversity/diversitycounts/
diversitycountstables2012.pdf
US demographics - 77% white, 13.4% Black, 18% Latinx

58. Ethics
Photo by Marc Schäfer on Unsplash

60. Jason Griffey
Director of
Strategic Initiatives
NISO
griffey@gmail.com
@griffey
http://jasongriffey.net
griffey@niso.org

61. Suggested Reading
Situating Methods in the Magic of Big Data and Artificial Intelligence by M.C. Elish and danah boyd
Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Noble
ARTIFICIAL UNINTELLIGENCE: How Computers Misunderstand the World by Meredith Broussard
Race After Technology: Abolitionist Tools for the New Jim Code by Ruha Benjamin
Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor by Virginia Eubanks
Weapons of Math Destruction by Cathy O’Neil
The Intersectional Internet: Race, Sex, Class, and Culture Online by Safiya Noble, Brendesha M. Tynes
Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech by Sara
Wachter-Boettcher
Data Feminism by Catherine D'Ignazio and Lauren F. Klein
So You Want to Talk About Race by Ijeoma Oluo
Design Justice: Community-Led Practices to Build the Worlds We Need by Sasha Costanza-Chock