Data Ethics - CAS act
Transcript of Data Ethics - CAS act
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Data Ethics
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Speakers
Peter Bothwell, FCASManaging Director, Data ScienceActuarial & Data ScienceAF Group
Tracy Spadola, CPCU, CIDM, FIDMVice PresidentStrategic Data OperationsISO / Verisk
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Data Ethics
What is Data Ethics?Impact from Big DataData Ethics vs Information EthicsOther Definitions of Data Ethics
Why NowRegulatory focusMinimize bias and social inequitiesCorporate and Public Perspective
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Data Ethics
Impact on Data ProfessionalsData Governance
Specialized Area of Governance
Codes of BehaviorCodes of Conduct exist for various professions
GuidelinesIncludes 3rd Party DataPersonally Identifiable DataData HandlingAlgorithms / Data ModelsBusiness Practices
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A Data Science Data Ethics Checklist
• Data Collection• Data Storage• Analysis• Modeling• Deployment
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But does this apply to actuaries?
• Code of Conduct• Principles• ASOP 23
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Collection
• Ensure Informed Consent
• Avoid Collection Bias
• Minimize PII
• License agreements• Cambridge Analytica
• Twitter polls• Modeling your book of business
• Oculus Quest 2• Billing Data
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• School used key-card to gather detailed behavioral data
• Gave data to a data science vendor
• Used advanced algorithms to identify at-risk students & recommend remediation
• Significant improvement in outcomes, but at what cost?
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Storage
• Security
• Privacy
• Retention
• Breaches• Stalking
• The right to be forgotten• CCPA requirements
• Facebook• Texts
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Analysis
• Blind Spots & Bias
• Honest Representation
• Auditability
• Gender presuppositions• Age vs Driving Experience
• Y-axis scope & scale• Anomalies and
transformations
• Documentation• Repeatability
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• Phone app that identifies song title and artist enhanced
• Able to identify both speaker and use internet to attach all available data
• Able to distinguish birth gender from identity gender and provide full history of changes
• How much should be invested to protect that information?
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Modeling
• Proxy Discrimination
• Metric Selection
• Communicate Weaknesses
• Credit Scoring• Territorial Rating
• Target variable choice• Reason messages
• Potential bias• Use beyond current book
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Deployment
• Redress & Rollback
• Concept Drift
• Unintended Use
• Model fixes & updates• Regulatory mandates
• Model inputs change behavior• Environment or process changes
• Model abuse• Agent gaming
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• AI chatbot trained to “sound” like cybercriminal
• Used to troll the dark web offering to buy stolen identities
• Only pursued against individuals with existing criminal investigations
• But does it entice criminal behavior and entrap?
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Conclusion
• None of this is new• But is it part of our Best Practices?
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Call to Action
Have the conversationReview Governance Programs
Final Message
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• Thank You!