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Ethics of Artificial (Narrow) Intelligence Nicholas Kalogirou, P.Eng | March 4 2020 "The real problem of humanity is the following: we have paleolithic emotions; medieval institutions; and god-like technology." - E.O. Wilson,


  1. Ethics of Artificial (Narrow) Intelligence Nicholas Kalogirou, P.Eng | March 4 2020

  2. "The real problem of humanity is the following: we have paleolithic emotions; medieval institutions; and god-like technology." - E.O. Wilson, Biologist

  3. Why does AI Ethics matter?

  4. What are we talking about?

  5. What are we talking about? A nested systems model

  6. What are we talking about? A nested systems model The current landscape

  7. What are we talking about? A nested systems model The current landscape What can we do?

  8. PART 1 WHAT ARE WE TALKING ABOUT

  9. "In their area of practice, engineers shall hold paramount the health, safety, and welfare of the public, and have regard for the environment." Guideline for Ethical Practice v2.2 Association of Professional Engineers and Geoscientists of Alberta (APEGA)

  10. How do you know whether you are doing more good than harm?

  11. ethics is the ongoing study, development, and application of moral reasoning

  12. today’s talk: the ethics of artificial narrow intelligence

  13. PART 2 NESTED SYSTEMS

  14. model

  15. model modeller

  16. model modeller organization

  17. get paid profit model modeller organization

  18. model modeller organization opp cost project failure

  19. model modeller organization societal systems

  20. entertainment tax revenue get paid profit model modeller organization societal systems

  21. model modeller organization societal systems opp cost project vulnerable population failure health + well being difficult to escape

  22. defense population health model modeller organization societal systems justice economy

  23. cosmic systems model modeller organization societal systems natural / life systems

  24. --waste --energy --CO2 refining AI natural / life systems

  25. --waste --energy --CO2 refining AI natural / life systems --waste ++production ++CO2

  26. direction of ethical progress

  27. "Models are opinions embedded in mathematics" - Cathy O'Neill , “Weapons of Math Destruction” start here modeller

  28. What do I value? What is my level of caring? What responsibility do I accept? modeller What do I have the courage to question? How do I verify my own knowledge?

  29. “we become what we pay attention to” modeller

  30. aristotle: focus on being a good person, modeller then right action will follow after

  31. “models should be more fair” asks modeller to develop fairness in self modeller

  32. modeller how do YOU adopt the identity, behavior, and attention of someone who is more fair?

  33. modeller virtues like fairness, accountability, truth, compassion, integrity

  34. “we become what we pay attention to” modeller

  35. look out modeller look in

  36. PART 3 THE CURRENT LANDSCAPE

  37. ACADEMICS DATA PRACTITIONERS TECHNOLOGISTS

  38. GOVERNMENTS ACADEMICS NON-PROFITS DATA PRACTITIONERS ACADEMIC INSTITUTIONS TECHNOLOGISTS CORPORATIONS

  39. frameworks checklists GOVERNMENTS interdisciplinary ACADEMICS research NON-PROFITS technical DATA tools PRACTITIONERS ACADEMIC INSTITUTIONS laws / regulation TECHNOLOGISTS deployment CORPORATIONS guidelines best practices

  40. Algorithmic frameworks Impact checklists Assessment GOVERNMENTS interdisciplinary information ACADEMICS research ethics NON-PROFITS technical XAI DATA tools PRACTITIONERS ACADEMIC INSTITUTIONS laws / regulation GDPR TECHNOLOGISTS deployment governance CORPORATIONS guidelines MILA best practices Statement

  41. Algorithmic frameworks Impact checklists Assessment GOVERNMENTS interdisciplinary information ACADEMICS research ethics NON-PROFITS technical XAI DATA tools PRACTITIONERS ACADEMIC INSTITUTIONS laws / regulation GDPR TECHNOLOGISTS deployment governance CORPORATIONS MILA guidelines Montreal best practices Declaration

  42. a review of AI ethics guidelines major themes fairness privacy accountability common good safety and transparency security inclusion explainability human social cohesion oversight

  43. 80% of guidelines include... fairness privacy accountability

  44. 80% of guidelines include... fairness privacy accountability significant technical efforts

  45. Where are the gaps? seeing ethical AI mainly as a technical problem collective action: self organization, right incentives, right public policy

  46. Where are the gaps? violating ethics standards and codes have no consequences technology outpaces the law | no professional bodies or regulations to reinforce behavior

  47. Where are the gaps? vague guidelines and little focus on self development reading guidelines tend to have no influence | developing our own self-responsibility and caring

  48. Where are the gaps? skipping ethics for profit and efficiency lack of time and resources for broader questioning

  49. Where are the gaps? seeing ethical AI mainly as a technical problem violating ethics standards and codes have no consequences vague guidelines and little focus on self development skipping ethics for profit and efficiency

  50. harmful consequences of AI - social trends INEQUALITY Worker / Employer Inequality Widening Socioeconomic Gaps Political / Democratic Disruption LOSS OF LIBERTY Privacy IS Liberty Increased control by corporations and government through surveillance

  51. PART 4 WHAT CAN WE DO

  52. DEVELOP SELF Caring ~ motivation not just technicals! WHAT CAN WE DO

  53. DEVELOP DISCUSS SELF VALUES Caring ~ motivation Openly discuss gap not just technicals! between values + action WHAT CAN WE DO

  54. DEVELOP DISCUSS SELF VALUES Caring ~ motivation Openly discuss gap not just technicals! between values + action WHAT CAN WE DO QUESTION BROADLY Who / what are we empowering? not just cost efficiency Keep asking

  55. DEVELOP DISCUSS SELF VALUES Caring ~ motivation Openly discuss gap not just technicals! between values + action WHAT CAN WE DO QUESTION PUBLIC BROADLY PRESSURE Who / what are we empowering? Hold harmful AI algorithms not just cost efficiency accountable Keep asking

  56. Data Analytics Lifecycle CRISP-DM Business Data Understanding Understanding Data Preparation Data Deployment Modeling Evaluation

  57. A Data Science Ethics Checklist https://deon.drivendata.org Start Informed consent Collection bias Limit PII Redress Roll back Data Deployment Concept drift Collection Unintended use Data security Right to be forgotten Data Data retention plan Storage Proxy discrimination Missing perspectives Modeling Analysis Fairness across groups Dataset bias Metric selection Honest representation Explainability Privacy in analysis Communicate bias Auditability

  58. Nick’s Ethics Compassion Set caring objective Process v0.1 Nested Accepted Explore impact of Choose to act Awareness Responsibility broader systems

  59. Nick’s Ethics Compassion Set caring objective Process v0.1 Nested Accepted Explore impact of Choose to act Awareness Responsibility broader systems Data Deployment Data Collection Data Storage Modeling Analysis Ethics Feedback Loop - ongoing study, revision, development of moral reasoning - develop oversight

  60. “We make our world significant by the courage of our questions, and the depth of our answers” - Carl Sagan earth

  61. References Alberta Boiler Safety Association. (n.d.). About Us. History of ABSA & Heritage: Heritage. https://www.absa.ca/about-absa/history-of-absa-heritage/heritage/ Crawford, K., Dobbe, R., Dryer, T., Fried, G., Green, B., Kaziunas, E., Kak, A., Mathur, V., McElroy, E., Sánchez , A. N., Raji, D., Rankin, J. L., Richardson, R., Schultz, J., West, S. M., & Whittaker, M. (2019). AI Now 2019 report. https://ainowinstitute.org/AI_Now_2019_Report.pdf Deon. (n.d.). An ethics checklist for data scientists. https://deon.drivendata.org Thanks. Gently. D. (2018). Pressure [Photograph]. Flickr. https://www.flickr.com/photos/6x7/25879810377/ Hagendorff, T. (2019). The ethics of AI ethics: An evaluation of guidelines. Minds & Machines. doi:10.1007/s11023-020-09517-8 contact | presentation | ethics resources Low, K. (2016). The Human Venture Institute mapbook (16th edition). Action Studies Institute. www.nickkal.com O’Neill, C. (2016). Weapons of math destruction. Crown Brooks. Provost, F., & Fawcett., T. (2014). Data science for business: What you need to know about data mining and data-analytic thinking. O’Reilly Media. The National Aeronautics and Space Administration. (2020). Pale blue dot revisited [Photograph]. Flickr. https://www.flickr.com/photos/nasacommons/49533887268/ Valerie. (2012). Tool usage [Photograph]. Flickr. https://www.flickr.com/photos/ucumari/7319932060/

  62. Selected License Attribution-NonCommercial-ShareAlike 4.0 International Except where otherwise noted, this work is licensed under https://creativecommons.org/licenses/by-nc-sa/4.0/

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