AI, as technical approach to solve problems, now is deploying in social systems and public administrations. What are the effects? the challenges? should we fear? What should we do?

1. Sandra Bermúdez
Welcome to the machine.
Buongiorno, Padova!
Algocracy and the state of AI
in public administrations.

2. Hello, I am Sandra.
bulabe.com
sandra@datank.ai
hola@bulabe.com

3. Mexico City.

5. The hype of AI.

6. AI has been here for longtime,
so what’s new?

7. Google Duplex Demo, May 2018.

8. Milano, Junio 2018.

9. AI into our pockets.

10. Computer Vision, AI O’Reilly Conference, NYC, April 2018.

11. Biometric identification (on our pockets right now).

12. And our systems.

13. Computer Vision. Faceprint scanning for boarding passes.
– https://www.bloomberg.com/news/articles/2017-05-31/jetblue-tests-using-face-recognition-to-scrap-boarding-passes

14. Computer Vision. Vehicle Face System.
– https://www.theverge.com/2017/5/9/15591648/airport-facial-recognition-customs-tsa-biometric-exit
Computers that never forget a face
– http://eepurl.com/dx4fNb

15. A Wall of AI.
Andoril, AI and Computer Vision for the Mexico-US border.
– https://www.wired.com/story/palmer-luckey-anduril-border-wall
Retrieving AI in public services and government agendas.

16. AI Approaches.
• AI as a quest to mimicry the human intelligence.
• AI as technical approach to solve problems (Statistical treatment, Data Science,
Machine Learning).
• AI as an intent to reformulate social practices.
The market practices pushing
the social dynamics.

17. AI, DS, ML as tech solutions deploying in
social systems and public administrations.

18. Criminal Justice System.
Criminal Risk Assessment, CrimeScan (Crime Predicting Software Tool),
PredPol (Spots patterns of criminal behavior).
Currently in use in USA
– https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
– https://www.documentcloud.org/documents/2702103-Sample-Risk-Assessment-COMPAS-CORE.html

19. Education. “Value-added modeling”, Scoring for professors.
Black boxes.
– https://www.washingtonpost.com/news/answer-sheet/wp/2016/05/10/judge-calls-evaluation-of-n-y-teacher-arbitrary-and-
capricious-in-case-against-new-u-s-secretary-of-education

20. Tax collection.
An italian case.
– http://taxinsights.ey.com/archive/archive-articles/innovation-deepens-insights-for-tax-authorities.aspx

21. • Governance is stressed by a radical force.
• Algocracy reaches maturity when every sphere of social interaction is mediated
by information technology.
• Governments transformed into data powered administrations.
• Math-Washing Public Administrations.
Algocracy.
John Danaher, Philosophical Disquisitions.
– http://philosophicaldisquisitions.blogspot.com/2014/01/rule-by-algorithm-big-data-and-threat.html
O’Neil, Cathy. Weapons of math destruction.
– https://weaponsofmathdestructionbook.com

22. • Level 0 - No automation. People powered public services.
• Level 1 - Simple augmentation. Data entry and profiling.
• Level 2 - Close supervision. Routine administration systems.
• Level 3 - Semi autonomous. Computers monitoring and running.
• Level 4 - Automation. A public service runs itself unless it hits an extreme case.
• Level 5 - Fully automated system which never requires human intervention.
Levels of AI in public services.
Stirling, Richard (2017)
– https://www.oxfordinsights.com/insights/2017/7/12/five-levels-of-ai-in-public-service

23. a. Precautionary principle - preemptively limit or even ban certain applications out of
fears for worst-case scenarios.
b. Permisionless innovation - prioritize experimentation and collaboration as the
default while addressing any issues that do arise as they go.
Police Makers.
Adam Thierer, Andrea Castillo O’Sullivan, and Raymond Russell. “Artificial Intelligence and Public Policy.”
– https://www.mercatus.org/publications/artificial-intelligence-public-policy
Which side are you?

24. • A corpus of algorithms and data-driven mechanisms that shapes the social and
political space and imposes severe constraints on how we mature politically
and socially.
• Habits, activities, and preferences are compiled, registered, and retrieved to
adapt an individual to a standardized behavior that aims at the highest possible
degree of compliance with the model patient, consumer, taxpayer,
employee, or citizen.
Invisible Barbed Wire.
Morosov, Evgeni. The Real Privacy Problem (MIT, 2013)
– https://www.technologyreview.com/s/520426/the-real-privacy-problem/

25. The model citizen.

26. The model citizen!

27. Social Credits and Reputation Systems.
Social Credit System ( yes, Wikipedia, :P )
– https://en.wikipedia.org/wiki/Social_Credit_System

28. China Citizens Score.
China’s New Tool for Social Control: A Credit Rating for Everything
– https://www.wsj.com/articles/chinas-new-tool-for-social-control-a-credit-rating-for-everything-1480351590

29. The datafied society.

30. From Science Fiction to Social Fiction.

31. • Digital security. The use of AI to automate tasks involved in carrying out
cyberattacks.
• Physical security. The use of AI to automate tasks involved in carrying out
attacks with drones and other physical systems that it would be infeasible to
direct remotely.
• Political security. The use of AI to automate tasks involved in surveillance,
persuasion (e.g. creating targeted propaganda), and deception (e.g.
manipulating videos).
The malicious use of Artificial Intelligence.
OPEN AI, University of Oxford, among others. February 2018
– http://arxiv.org/pdf/1802.07228.pdf

32. After dystopia.

33. The market solution.
• Open source and Explainable AI.
• Privacy tools.
• Data economy (platforms should pay us for our data or pay for service).
Crawford, Kate. The problem with bias.
– https://www.youtube.com/watch?v=6Uao14eIyGc
“The user sets her preferences once, specifies how she would negotiate privacy and what she is willing to
give up, and from that moment on, when she enters a site, the site and her machine negotiate. Only if the
machines can agree will the site be able to obtain her personal data.”
– L. Lessig, Code and others laws of cyberspace

34. Data Selfie.
– https://dataselfie.it/#/

35. Privacy browser extensions.

36. Sabotage (neo ludditism).
• A bold refusal to participate in tracking and self quantifying.
• Data lakes poisoning (extremism).
• Provocative products and resistance artifacts.
2018, The year of neo luddite
– https://www.theguardian.com/technology/2018/mar/04/will-2018-be-the-year-of-the-neo-luddite
Di Salvo, Carl.
– Adversarial Design, Cambridge: MIT Press, 2012

37. Privacy threats and countermeasures.
– https://www.postscapes.com/internet-of-things-privacy-threats-and-countermeasures
How to hide from machines
– http://dismagazine.com/dystopia/evolved-lifestyles/8115/anti-surveillance-how-to-hide-from-machines

38. The civic solution.
• Organizations (Data privacy and ML accountability).
• Tools for AI governance (Algorithmic Impact Assessment).
• Laws and regulations (GDPR, Consumer Privacy Act)
AI NOW, NYC
– https://ainowinstitute.org
PRIVACY INTERNATIONAL
– http://privacyinternational.org
OPEN DATA INSTITUTE
– https://theodi.org/about-the-odi/

39. Algorithmic Impact Assessment.
• Respect the public’s right to know which systems impact their lives.
• Ensure greater accountability of algorithmic systems by providing a meaningful
and ongoing opportunity for external researchers to review and audit.
• Increase public agencies internal expertise and capacity to evaluate the systems
so that they can anticipate issues that might raise concerns.
• Ensure that the public has a meaningful opportunity to respond to and, if
necessary, dispute an agency’s approach to algorithmic accountability.
– https://medium.com/@AINowInstitute/algorithmic-impact-assessments-toward-accountable-automation-in-public-agencies-bd9856e6fdde

40. Towards a Human Centered AI.
• Human Centered Design tools and frameworks.
• Literacy from stakeholders (specially people in governments).
• Commitment from developers, vendors, and enterprises.
• Standardization of good practices.

41. Hello, I am Sandra.
bulabe.com
sandra@datank.ai
hola@bulabe.com

42. –
Sandra Bermúdez, designer. Mexico City based product manager and researcher.
Currently Head of Product at Datank.ai, a Data Science company that helps enterprises
to develop Artificial Intelligence strategies and Machine Learning solutions.