Course curriculum

    1. Welcome

    1. Reference List

    2. Summary of AI Risk Management Resources

    1. Section Overview

    1. 1. Discrimination-Free Insurance Pricing

    2. 2. Anti-Discrimination Insurance Pricing: Regulations, Fairness Criteria, and Models

    3. 3. The Fairness of Machine Learning in Insurance: New Rags for an Old Man?

    4. 4. Designing Fairly Fair Classifiers Via Economic Fairness Notions

    5. 5. Paradoxes in Fair Machine Learning

    6. 6. A Multi-Task Network Approach for Calculating Discrimination-Free Insurance Prices

    7. 7. Insurers and regulators must stamp out discrimination in insurance pricing to ensure fairness for consumers

    8. 8. Limits and concepts of the indirect discrimination

    9. 9. IFoA: The hidden risks of being poor

    10. 10. Auditing the AI Auditors: A Framework for Evaluating Fairness and Bias in High Stakes AI Predictive Models

    11. 11. Semi-supervised learning in insurance : fairness and active learning

    12. 12. Fairness and Calibration in predictive models

    13. 13. AI and Ethics in Insurance: A New Solution to Mitigate Proxy Discrimination in Risk Modelling (EAA e-Conference 2023)

    14. 14. What is Fair? Proxy Discrimination vs. Demographic Disparities in Insurance Pricing

    15. 15. Reducing bias in AI-based financial services

    16. 16. How insurers can mitigate the discrimination risks posed by AI

    17. 17. Bias in AI: What it is, Types, Examples & 6 Ways to Fix it in 2022

    18. 18. Equality of Opportunity in Supervised Learning

    19. 19. Fairlearn: Open source community driven project to help data scientist with the fariness of AI sysytems

    20. 20. Model Fairness Assessment: The Fairness dashboard

    21. 21. Unnatural Bias: When AI is Over Assumptive

    1. 1. IFoA APS X1: Applying Standards to Actuarial Work

    2. 2. Exploring Ethics & AI within Financial Services Webinar

    3. 3. Royal Statistical Society Paper - Additional information and resources: ethical data science

    4. 4. Ethical Use of Artificial Intelligence for Actuaries - SOA

    5. 5. AI Ethics - DataRobot

    6. 6. Principles of AI Ethics (SAS GI Seminar 2019)

    7. 7. A Guide for Ethical Data Science

    8. 8. IEEE - Ethics in Action in Autonomous and Intelligent Systems

    9. 9. IEEE - Ethically Aligned Design

    10. 10. AI and ethics in insurance: a new solution to mitigate proxy discrimination in risk modeling

    11. 11. Data & Ethics: How to establish a sustainable, data-driven business model in life insurance

    12. 12. Responsible Use of Data in the Digital Age: Customer expectations and insurer responses

    13. 13. ProActuary: Culture, Professionalism and Digital Actuary

    14. 14. AI risk and ethics forum series: Trustworthy AI

    15. 15. IFoA APS X2: Review of Actuarial Work

    16. 16. NZ Police - Safe and ethical use of algorithms

    17. 17. AI Ethics and Insurance (SOA)

    18. 18. Changing Landscapes in the Health and Life Sciences: Ethical Challenges of Big Data

    1. 1. LASSO regularisation within the LocalGLMnet architecture

    2. 2. Mind the Gap - Safely Incorporating Deep Learning Models into the Actuarial Toolkit by Ronald Richman

    3. 3. Beyond theoretical data science: application to actuarial work: lapse analysis

    4. 4. ADS Actuarial Data Science An initiative of the Swiss Association of Actuaries

    5. 5. Bayesian Neural Network perspectives - CAS

    6. 6. Big data and data profiling in the insurance industry - RPC

    7. 7. Believing the bot – model risk in the era of deep learning

    8. 8. Generative neural networks for synthetic data generation in insurance: context and use cases

    9. 9. Machine learning in UK financial services - Bank of England

    10. 10. EBA Discussion paper on Machine Learning for IRB Models

    11. 11. Canadian Mortality Table Construction Alternative Methods - Generalized Additive Model and Neural Network Model

    12. 12. Machine learning in Reserving Working Party - UK survey findings (IFoA)

    13. 13. Machine Learning: A Practical Guide To Managing Risk

    14. 14. Risk Management Framework for Machine Learning Security

    15. 15. Lessons from data science in Healthcare & their potential applications for insurers

    16. 16. Quantifying uncertainty of machine learning methods for loss given default

About this course

  • Free
  • 204 lessons
  • 3.5 hours of video content