Ethics of Artificial (Narrow) Intelligence Nicholas Kalogirou, P.Eng - - PowerPoint PPT Presentation

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Ethics of Artificial (Narrow) Intelligence Nicholas Kalogirou, P.Eng - - PowerPoint PPT Presentation

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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,

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Ethics of Artificial (Narrow) Intelligence

Nicholas Kalogirou, P.Eng | March 4 2020

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"The real problem of humanity is the following: we have paleolithic emotions; medieval institutions; and god-like technology."

  • E.O. Wilson, Biologist
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Why does AI Ethics matter?

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What are we talking about?

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What are we talking about? A nested systems model

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

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

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PART 1 WHAT ARE WE TALKING ABOUT

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"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)

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

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

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today’s talk: the ethics of artificial narrow intelligence

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PART 2 NESTED SYSTEMS

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model

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modeller model

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  • rganization

modeller model

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  • rganization

modeller model

get paid profit

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  • rganization

modeller model

  • pp cost

project failure

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societal systems

  • rganization

modeller model

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societal systems

  • rganization

modeller model

get paid profit entertainment tax revenue

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societal systems

  • rganization

modeller model

  • pp cost

project failure vulnerable population health + well being difficult to escape

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societal systems

  • rganization

modeller model

economy justice population health defense

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societal systems

  • rganization

modeller model natural / life systems cosmic systems

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natural / life systems

  • -waste
  • -energy
  • -CO2

refining AI

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natural / life systems

  • -waste
  • -energy
  • -CO2
  • -waste

++production ++CO2 refining AI

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direction of ethical progress

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modeller

start here

"Models are opinions embedded in mathematics"

  • Cathy O'Neill , “Weapons of Math Destruction”
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modeller

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

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modeller

“we become what we pay attention to”

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modeller

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

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modeller

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

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modeller

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

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modeller

virtues like fairness, accountability, truth, compassion, integrity

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modeller

“we become what we pay attention to”

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modeller

look out look in

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PART 3 THE CURRENT LANDSCAPE

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DATA PRACTITIONERS ACADEMICS TECHNOLOGISTS

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DATA PRACTITIONERS NON-PROFITS GOVERNMENTS ACADEMIC INSTITUTIONS CORPORATIONS ACADEMICS TECHNOLOGISTS

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

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

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

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accountability privacy fairness transparency safety and security common good human

  • versight

explainability inclusion social cohesion a review of AI ethics guidelines major themes

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accountability privacy fairness 80% of guidelines include...

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

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Where are the gaps? seeing ethical AI mainly as a technical problem

collective action: self organization, right incentives, right public policy

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Where are the gaps? violating ethics standards and codes have no consequences

technology outpaces the law | no professional bodies or regulations to reinforce behavior

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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

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Where are the gaps? skipping ethics for profit and efficiency

lack of time and resources for broader questioning

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

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harmful consequences of AI - social trends INEQUALITY LOSS OF LIBERTY

Privacy IS Liberty Increased control by corporations and government through surveillance Worker / Employer Inequality Widening Socioeconomic Gaps Political / Democratic Disruption

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PART 4 WHAT CAN WE DO

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WHAT CAN WE DO DEVELOP SELF

Caring ~ motivation not just technicals!

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WHAT CAN WE DO DEVELOP SELF DISCUSS VALUES

Caring ~ motivation not just technicals! Openly discuss gap between values + action

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WHAT CAN WE DO DEVELOP SELF DISCUSS VALUES QUESTION BROADLY

Caring ~ motivation not just technicals! Who / what are we empowering? not just cost efficiency Keep asking Openly discuss gap between values + action

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WHAT CAN WE DO DEVELOP SELF DISCUSS VALUES QUESTION BROADLY PUBLIC PRESSURE

Caring ~ motivation not just technicals! Who / what are we empowering? not just cost efficiency Keep asking Openly discuss gap between values + action Hold harmful AI algorithms accountable

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Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment Data

Data Analytics Lifecycle CRISP-DM

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Data Collection Data Storage Deployment Informed consent Collection bias Limit PII Analysis Modeling Proxy discrimination Fairness across groups Metric selection Explainability Communicate bias Redress Roll back Concept drift Unintended use Data security Right to be forgotten Data retention plan Missing perspectives Dataset bias Honest representation Privacy in analysis Auditability

A Data Science Ethics Checklist

https://deon.drivendata.org Start

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Compassion Nested Awareness Accepted Responsibility Set caring objective

Nick’s Ethics Process v0.1

Explore impact of broader systems Choose to act

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Data Collection Data Storage Deployment Data Compassion Nested Awareness Analysis Modeling Accepted Responsibility Set caring objective

Nick’s Ethics Process v0.1

Ethics Feedback Loop

  • ongoing study, revision,

development of moral reasoning

  • develop oversight

Explore impact of broader systems Choose to act

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“We make our world significant by the courage of our questions, and the depth of our answers”

  • Carl Sagan

earth

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References Thanks.

contact | presentation | ethics resources www.nickkal.com

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
  • 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 Low, K. (2016). The Human Venture Institute mapbook (16th edition). Action Studies Institute. 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/

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