Article 25. Algorithmic System Integrity: Explainability (Part 2) - Complexity

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 among the techniques we can use. To subscribe to the weekly articles: https://riskinsights.com.au/blog#subscribeAbout this podcastA 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).

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 information, preserving proprietary algorithms, and securing fraud detection systems.Focusing on what audiences need, with a few specific considerations, can help address these. To subscribe to the weekly articles: https://riskinsights.com.au/blog#subscribeAbout this podcastA 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).

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 considerations that don't require us to select any particular definition. To subscribe to the weekly articles: https://riskinsights.com.au/blog#subscribeAbout this podcastA 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).

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 processes simpler. To subscribe to the weekly articles: https://riskinsights.com.au/blog#subscribeAbout this podcastA 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).

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 “visibility” techniques can be useful. To subscribe to the weekly articles: https://riskinsights.com.au/blog#subscribeAbout this podcastA 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).

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: Complex algorithms, intricate workflows, privacy concerns, and making explanations understandable for all stakeholders.What’s Next: Future articles will explore practical solutions to these challenges. To subscribe to the weekly articles: https://riskinsights.com.au/blog#subscribeAbout this podcastA 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).

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). To subscribe to the weekly articles: https://riskinsights.com.au/blog#subscribeAbout this podcastA 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).

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). To subscribe to the weekly articles: https://riskinsights.com.au/blog#subscribeAbout this podcastA 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).

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 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 Markers00:00 Introduction to Dr. Shea Brown and BABL AI00:36 The Journey from Astrophysics to AI Auditing02:22 Core Services and Compliance Audits at BABL03:57 Educational Initiatives and AI Literacy05:48 Collaborations and Professional Organizations08:57 Approach to AI Audits and Readiness17:29 Challenges with Generative AI in Audits29:21 Trends in AI Deployment and Risk Assessment34:53 Skills and Training for AI Governance40:15 Conclusion and Contact Information To subscribe to the weekly articles: https://riskinsights.com.au/blog#subscribeAbout this podcastA 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).

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 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. To subscribe to the weekly articles: https://riskinsights.com.au/blog#subscribeAbout this podcastA 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).