Nicholas Kalogirou, P.Eng | March 4 2020nk1/wybar/AI Ethics... · 2020-03-17 · accountability...

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

Transcript of Nicholas Kalogirou, P.Eng | March 4 2020nk1/wybar/AI Ethics... · 2020-03-17 · accountability...

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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 1WHAT 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 2NESTED SYSTEMS

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model

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modellermodel

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organizationmodellermodel

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organizationmodellermodel

get paid profit

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organizationmodellermodel

opp cost projectfailure

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societal systemsorganizationmodellermodel

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societal systemsorganizationmodellermodel

get paid profit

entertainment

tax revenue

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societal systemsorganizationmodellermodel

opp cost projectfailure

vulnerable populationhealth + well being

difficult to escape

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societal systemsorganizationmodellermodel

economy

justice

population health

defense

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societal systemsorganizationmodellermodel natural / life systems

cosmicsystems

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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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modellerstart 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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modelleraristotle: 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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modellerlook outlook in

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PART 3THE CURRENT LANDSCAPE

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DATAPRACTITIONERS

ACADEMICS

TECHNOLOGISTS

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DATAPRACTITIONERS

NON-PROFITS

GOVERNMENTS

ACADEMICINSTITUTIONS

CORPORATIONS

ACADEMICS

TECHNOLOGISTS

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DATAPRACTITIONERS

NON-PROFITS

GOVERNMENTS

ACADEMICINSTITUTIONS

CORPORATIONS

guidelinesbest practices

frameworkschecklists

technicaltools

laws / regulation

interdisciplinaryresearch

deployment

ACADEMICS

TECHNOLOGISTS

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DATAPRACTITIONERS

NON-PROFITS

GOVERNMENTS

ACADEMICINSTITUTIONS

CORPORATIONS

guidelinesbest practices

frameworkschecklists

technicaltools

laws / regulation

interdisciplinaryresearch

deployment

ACADEMICS

TECHNOLOGISTSGDPR

XAI

Algorithmic Impact

Assessment

informationethics

governance

MILAStatement

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DATAPRACTITIONERS

NON-PROFITS

GOVERNMENTS

ACADEMICINSTITUTIONS

CORPORATIONS

guidelinesbest practices

frameworkschecklists

technicaltools

laws / regulation

interdisciplinaryresearch

deployment

ACADEMICS

TECHNOLOGISTSGDPR

XAI

Algorithmic Impact

Assessment

informationethics

governance

MILAMontreal

Declaration

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accountability privacy fairness

transparency safety and security

common good

human oversight

explainability inclusionsocial cohesion

a review of AI ethics guidelinesmajor 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 problemcollective action: self organization, right incentives, right public policy

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Where are the gaps?

violating ethics standards and codes have no consequencestechnology 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 developmentreading 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 LIBERTYPrivacy IS LibertyIncreased control by corporations and government through surveillance

Worker / Employer InequalityWidening Socioeconomic GapsPolitical / Democratic Disruption

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PART 4WHAT CAN WE DO

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WHAT CAN WE DO

DEVELOP SELF

Caring ~ motivationnot just technicals!

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WHAT CAN WE DO

DEVELOP SELF

DISCUSS VALUES

Caring ~ motivationnot just technicals!

Openly discuss gap between values + action

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WHAT CAN WE DO

DEVELOP SELF

DISCUSS VALUES

QUESTION BROADLY

Caring ~ motivationnot 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 ~ motivationnot 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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BusinessUnderstanding

DataUnderstanding

DataPreparation

Modeling

Evaluation

DeploymentData

Data AnalyticsLifecycleCRISP-DM

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DataCollection

DataStorage

Deployment

Informed consentCollection biasLimit PII

AnalysisModelingProxy discriminationFairness across groupsMetric selectionExplainabilityCommunicate bias

RedressRoll backConcept driftUnintended use Data security

Right to be forgottenData retention plan

Missing perspectivesDataset biasHonest representationPrivacy in analysisAuditability

A Data Science Ethics Checklisthttps://deon.drivendata.org Start

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Compassion

NestedAwareness

AcceptedResponsibility

Set caring objectiveNick’s EthicsProcess v0.1

Explore impact of broader systems

Choose to act

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DataCollection

DataStorage

Deployment Data

Compassion

NestedAwareness

AnalysisModeling

AcceptedResponsibility

Set caring objectiveNick’s EthicsProcess 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 resourceswww.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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