Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing
Risk Insights: Yusuf Moolla
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About This Podcast
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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Recent Episodes
S1E32 Article 29. Algorithmic System Integrity: Explainability (Part 6) - Interpretability
Spoken by a human version of this article.TL;DR (TL;DL?)Technical stakeholders need detailed explanations.Non-technical stakeholders need plain language.Visuals, layering, literacy, and feedback are…
S1E31 Article 28. Algorithmic System Integrity: Explainability (Part 5) - Privacy and Confidentiality
Spoken by a human version of this article.TL;DR (TL;DL?)Algorithmic systems create challenges in balancing explainability with privacy and confidentiality.Key challenges include protecting sensitive…
S1E30 Article 27. Algorithmic System Integrity: Explainability (Part 4)
Spoken by a human version of this article.TL;DR (TL;DL?)Explainability is necessary to build trust in AI systems.There is no universally accepted definition of explainability.So we focus on key…
S1E29 Article 26. Algorithmic System Integrity: Explainability (Part 3) - Complicated Processes
Spoken by a human version of this article.TL;DR (TL;DL?)Algorithmic processes are often complicated by intricate data flows and transformations.Data flow diagrams and documentation can help make…
S1E28 Article 25. Algorithmic System Integrity: Explainability (Part 2) - Complexity
Spoken by a human version of this article.TL;DR (TL;DL?)Complexity must be actively managed rather than passively accepted.Data relevance directly impacts both accuracy and explainability.Technical…
S1E27 Article 24. Algorithmic System Integrity: Explainability (Part 1)
Spoken by a human version of this article.TL;DR (TL;DL?)Why Explainability Matters: It builds trust, is needed to meet compliance obligations, and can help identify errors faster.Key Challenges:…
S1E26 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…
S1E25 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…
S1E24 Guest 3. Shea Brown, Founder and CEO of BABL AI
Navigating AI Audits with Dr. Shea BrownDr. Shea Brown is Founder and CEO of BABL AI BABL specializes in auditing and certifying AI systems, consulting on responsible AI practices, and offering…
S1E23 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.LinksEU AI…
S1E22 Guest 2. Patrick Sullivan: VP of Strategy and Innovation at A-LIGN
Navigating AI Governance and CompliancePatrick 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.…
S1E21 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…
S1E20 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…
S1E19 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…
S1E18 Article 18. Algorithm Integrity: Training and Awareness
Spoken by a human version of this article.Ongoing education helps everyone understand their role in responsibly developing and using algorithmic systems.Regulators and standard-setting bodies…
S1E17 Article 17. Algorithm Integrity: Audit vs Review
Spoken by a human version of this article.The terminology – “audit” vs “review” - is important, but clarity about deliverables is more important when commissioning algorithm integrity…
S1E16 Article 16. Algorithmic System Accuracy Reviews – Choosing the Right Approach
Spoken (by a human) version of this article.Outcome-focused accuracy reviews directly verify results, offering more robust assurance than process-focused methods.This approach can catch translation…
S1E15 Article 15. Algorithm Integrity Documentation - Getting Started
Spoken (by a human) version of this article.Documentation makes it easier to consistently maintain algorithm integrity.This is well known.But there are lots of types of documents to prepare, and…
S1E14 Article 14. External data - use with care
Spoken (by a human) version of this article.Banks and insurers are increasingly using external data; using them beyond their intended purpose can be risky (e.g. discriminatory).Emerging regulations…
S1E13 Article 13. Bridging the purpose-risk gap: Customer-first algorithmic risk assessments
Spoken (by a human) version of this article.Banks and insurers sometimes lose sight of their customer-centric purpose when assessing AI/algorithm risks, focusing instead on regular business risks and…
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Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing has published 33 episodes since August 2024, covering topics in Business, Management.
Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing is currently moderate with new episodes every few days. Average episode length is 8m.
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