Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

Risk Insights: Yusuf Moolla
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Insights for financial services leaders who want to enhance fairness and accuracy in their use of data, algorithms, and AI.   Each episode explores challenges and solutions related to algorithmic integrity, including discussions on navigating independent audits.   The goal of this podcast is to give leaders the knowledge they need to ensure their data practices benefit customers and other stakeholders, reducing the potential for harm and upholding industry standards.

  1. FEB 21

    Article 23. Algorithmic System Integrity: Testing

    Spoken by a human version of this article. TL;DR (TL;DL?) Testing is a core basic step for algorithmic integrity.Testing involves various stages, from developer self-checks to UAT. Where these happen will depend on whether the system is built in-house or bought.Testing needs to cover several integrity aspects, including accuracy, fairness, security, privacy, and performance.Continuous testing is needed for AI systems, differing from traditional testing due to the way these newer systems change (without code changes). About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla. Produced by Risk Insights (riskinsights.com.au).

    6 min

  2. FEB 16

    Article 22. Algorithm Integrity: Third party assurance

    Spoken by a human version of this article. One question that comes up often is “How do we obtain assurance about third party products or services?” Depending on the nature of the relationship, and what you need assurance for, this can vary widely. This article attempts to lay out the options, considerations, and key steps to take. TL;DR (TL;DL?) Third-party assurance for algorithm integrity varies based on the nature of the relationship and specific needs, with several options.Key factors to consider include the importance and risk level of the service/product, regulatory expectations, complexity, transparency, and frequency of updates.Standardised assurance frameworks for algorithm integrity are still emerging; adopt a risk-based approach, and consider sector-specific standards like CPS230(Australia). About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla. Produced by Risk Insights (riskinsights.com.au).

    7 min

  3. JAN 31

    Guest 3. Shea Brown, Founder and CEO of BABL AI

    Navigating AI Audits with Dr. Shea Brown Dr. Shea Brown is Founder and CEO of BABL AI BABL specializes in auditing and certifying AI systems, consulting on responsible AI practices, and offering online education. Shea shares his journey from astrophysics to AI auditing, the core services provided by BABL AI including compliance audits, technical testing, and risk assessments, and the importance of governance in AI. He also addresses the challenges posed by generative AI, the need for continuous upskilling in AI literacy, and the role of organizations like the IAAA and For Humanity in building consensus and standards in AI auditing. Finally, Shea provides insights on third-party risks, in-house AI developments, and key skills needed for effective AI governance. Chapter Markers 00:00 Introduction to Dr. Shea Brown and BABL AI 00:36 The Journey from Astrophysics to AI Auditing 02:22 Core Services and Compliance Audits at BABL 03:57 Educational Initiatives and AI Literacy 05:48 Collaborations and Professional Organizations 08:57 Approach to AI Audits and Readiness 17:29 Challenges with Generative AI in Audits 29:21 Trends in AI Deployment and Risk Assessment 34:53 Skills and Training for AI Governance 40:15 Conclusion and Contact Information About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla. Produced by Risk Insights (riskinsights.com.au).

    41 min

  4. JAN 31

    Article 21. AI Risk Training: Role-based tailoring

    Spoken by a human version of this article. AI literacy is growing in importance (e.g., EU AI Act, IAIS). AI literacy needs vary across roles. Even "AI professionals" need AI Risk training. Links EU AI Act: The European Union Artificial Intelligence Act - specific expectation about “AI literacy”.IAIS: The International Association of Insurance Supervisors is developing a guidance paper on the supervision of AI.About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla. Produced by Risk Insights (riskinsights.com.au).

    6 min

  5. JAN 21

    Guest 2. Patrick Sullivan: VP of Strategy and Innovation at A-LIGN

    Navigating AI Governance and Compliance Patrick Sullivan is Vice President of Strategy and Innovation at A-LIGN and an expert in cybersecurity and AI compliance with over 25 years of experience. Patrick shares his career journey, discusses his passion for educating executives and directors on effective governance, and explains the critical role of management systems like ISO 42001 in AI compliance. We discuss the complexities of AI governance, risk assessment, and the importance of clear organizational context. Patrick also highlights the challenges and benefits of AI assurance and offers insights into the changing landscape of AI standards and regulations. Chapter Markers 00:00 Introduction 00:23 Patrick's Career Journey 02:31 Focus on AI Governance 04:19 Importance of Education and Internal Training 08:08 Involvement in Industry Associations 14:13 AI Standards and Governance 20:06 Challenges with preparing for AI Certification 28:04 Future of AI Assurance About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla. Produced by Risk Insights (riskinsights.com.au).

    32 min

  6. JAN 20

    Guest 1. Ryan Carrier: Executive Director of ForHumanity

    Mitigating AI Risks Ryan Carrier is founder and executive director of ForHumanity, a non-profit focused on mitigating the risks associated with AI, autonomous, and algorithmic systems. With 25 years of experience in financial services, Ryan discusses ForHumanity's mission to analyze and mitigate the downside risks of AI to benefit society. The conversation includes insights on the foundation of ForHumanity, the role of independent AI audits, educational programs offered by the ForHumanity AI Education and Training Center, AI governance, and the development of audit certification schemes. Ryan also highlights the importance of AI literacy, stakeholder management, and the future of AI governance and compliance. Chapter Markers 00:00 Introduction to Ryan Carrier and ForHumanity 00:57 Ryan's Background and Journey to AI 02:10 Founding ForHumanity: Mission and Early Challenges 05:15 Developing Independent Audits for AI 08:02 ForHumanity's Role and Activities 17:26 Education Programs and Certifications 29:21 AI Literacy and Future of Independent Audits 42:06 Getting Involved with ForHumanity About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla. Produced by Risk Insights (riskinsights.com.au).

    45 min

  7. JAN 15

    Article 20. Algorithm Reviews: Public vs Private Reports

    Spoken (by a human) version of this article. Public AI audit reports aren't universally required; they mainly apply to high-risk applications and/or specific jurisdictions.The push for transparency primarily concerns independent audits, not internal reviews.Prepare by implementing ethical AI practices and conducting regular reviews.Note: High-risk AI systems in banking and insurance are subject to specific requirements Links AI and algorithm audit guidelines vary widely and are not universally applicable. We discussed this in a previous article, outlining how the appropriateness of audit guidance depends on your circumstances.Audit vs Review: we explored this topic in depth in a previous article. About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla. Produced by Risk Insights (riskinsights.com.au).

    8 min

  8. JAN 13

    Article 19. Algorithmic System Reviews: Substantive vs. Controls Testing

    Spoken by a human version of this article. Knowing the basics of substantive testing vs. controls testing can help you determine if the review will meet your needs.Substantive testing directly identifies errors or unfairness, while controls testing evaluates governance effectiveness. The results/conclusions are different.Understanding these differences can also help you anticipate the extent of your team's involvement during the review process. Links  This article details a (largely) substantive testing method for accuracy reviews. About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yusuf Moolla. Produced by Risk Insights (riskinsights.com.au).

    6 min
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Insights for financial services leaders who want to enhance fairness and accuracy in their use of data, algorithms, and AI.   Each episode explores challenges and solutions related to algorithmic integrity, including discussions on navigating independent audits.   The goal of this podcast is to give leaders the knowledge they need to ensure their data practices benefit customers and other stakeholders, reducing the potential for harm and upholding industry standards.

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