Những podcast Algorithm Integrity hay nhất (2025)

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Article 23. Algorithmic System Integrity: Testing 5:47

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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, securi…

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Article 22. Algorithm Integrity: Third party assurance 7:26

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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?) Th…

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Guest 3. Shea Brown, Founder and CEO of BABL AI 41:23

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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 te…

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Article 21. AI Risk Training: Role-based tailoring 6:26

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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 Supe…

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Guest 2. Patrick Sullivan: VP of Strategy and Innovation at A-LIGN 32:03

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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 …

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Guest 1. Ryan Carrier: Executive Director of ForHumanity 44:49

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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 conv…

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Article 20. Algorithm Reviews: Public vs Private Reports 8:26

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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-ri…

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Article 19. Algorithmic System Reviews: Substantive vs. Controls Testing 6:26

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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 diffe…

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Article 18. Algorithm Integrity: Training and Awareness 4:07

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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 emphasise the need for AI literacy across all organisational levels. Links ForHumanity - join the growing community here. ForHumanity - free courses here. I…

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Article 17. Algorithm Integrity: Audit vs Review 9:10

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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 assessments. Audits are formal, with an opinion or conclusion that can often be shared externally. Reviews come in various forms and typically produce recommendation…

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Article 16. Algorithmic System Accuracy Reviews – Choosing the Right Approach 8:07

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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 errors, unintended consequences, and edge cases that process reviews might miss. While more time-consuming and complex, outcome-focused reviews provid…

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Article 15. Algorithm Integrity Documentation - Getting Started 5:19

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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 often the first hurdle is just thinking about where to start. So this simple guide is meant to help do exactly that – get going. About this podcast A po…

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Article 14. External data - use with care 6:59

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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 and regulatory guidance emphasise the need for active oversight by boards, senior management to ensure responsible use of external data. Keeping the c…

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Article 13. Bridging the purpose-risk gap: Customer-first algorithmic risk assessments 7:18

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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 regulatory concerns. Regulators are noticing this disconnect. This article aims to outline why the disconnect happens and how we can fix it. Report m…

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Article 12. Risk-Focused Principles for Change Control in Algorithmic Systems 12:00

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Spoken (by a human) version of this article. With algorithmic systems, an change can trigger a cascade of unintended consequences, potentially compromising fairness, accountability, and public trust. So, managing changes is important. But if you use the wrong framework, your change control process may tick the boxes, but be both ineffective and ine…

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Article 11. Deprovisioning User Access to Maintain Algorithm Integrity 9:27

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Spoken (by a human) version of this article. The integrity of algorithmic systems goes beyond accuracy and fairness. In Episode 4, we outlined 10 key aspects of algorithm integrity. Number 5 in that list (not in order of importance) is Security: the algorithmic system needs to be protected from unauthorised access, manipulation and exploitation. In…

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Article 10. Fairness reviews: identifying essential attributes 6:54

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Spoken (by a human) version of this article. When we're checking for fairness in our algorithmic systems (incl. processes, models, rules), we often ask: What are the personal characteristics or attributes that, if used, could lead to discrimination? This article provides a basic framework for identifying and categorising these attributes. About thi…

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Article 9. Algorithmic Integrity: Don't wait for legislation 10:39

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Spoken (by a human) version of this article. Legislation isn't the silver bullet for algorithmic integrity. Are they useful? Sure. They help provide clarity and can reduce ambiguity. And once a law is passed, we must comply. However: existing legislation may already apply new algorithm-focused laws can be too narrow or quickly outdated standards ca…

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Article 8. A Balanced Focus on New and Established Algorithms 8:50

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Spoken (by a human) version of this article. Even in discussions among AI governance professionals, there seems to be a silent “gen” before AI. With rapid progress - or rather prominence – of generative AI capabilities, these have taken centre stage. Amidst this excitement, we mustn't lose sight of the established algorithms and data-enabled workfl…

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Article 7. Postcodes: Hidden Proxies for Protected Attributes 11:31

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Spoken (by a human) version of this article. In a previous article, we discussed algorithmic fairness, and how seemingly neutral data points can become proxies for protected attributes. In this article, we'll explore a concrete example of a proxy used in insurance and banking algorithms: postcodes. We've used Australian terminology and data. But th…

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Article 6. Balancing Security and Access for increased algorithmic integrity 5:41

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Spoken (by a human) version of this article. When we talk about security in algorithmic systems, it's easy to focus solely on keeping the bad guys out. But there's another side to this coin that's just as important: making sure the right people can get in. This article aims to explain how security and access work together for better algorithm integ…

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Article 5. Equal vs Equitable: Algorithmic Fairness 13:53

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Spoken (by a human) version of this article. Fairness in algorithmic systems is a multi-faceted, and developing, topic. In episode 4, we explored ten key aspects to consider when scoping an algorithm integrity audit. One aspect was fairness, with this in the description: "...The design ensures equitable treatment..." This raises an important questi…

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Article 4. Structuring the Audit Objective: 10 Key Aspects of Algorithm Integrity 12:56

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Spoken (by a human) version of this article. In Episode 1, we explored the challenges of placing undue reliance on audits. One potential solution that we outlined is a clear scope, particularly regarding the audit objective. In this episode, we focus on algorithm integrity as the broad audit objective. While it’s easy to assert that an algorithm ha…

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Article 3. Navigate Algorithm Audit Guidance: some aren't relevant to your context 8:55

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Spoken (by a human) version of this article. AI and algorithm audits help ensure ethical and accurate data processing, preventing harm and disadvantage. However, the guidelines are not yet mature, and quite disparate. This can make the audit process confusing, and quite daunting - how do you wade through it all to find the information that you need…

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Article 2. Choice vs obligation: motivation shapes the effectiveness of your review 6:53

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Spoken (by a human) version of this article. The motivation(s) for commissioning a review can determine how effective it will be. Consider a personal health check-up: Sometimes we undergo medical check-ups because we don’t have a choice. We need to - for example for workplace requirements or for insurance. At other times, we choose to undergo such …

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Article 1. How reliable is the algorithm review that you have commissioned? 10:14

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Spoken (by a human) version of this article. One common issue with audits is undue reliance. Can you rely on the audit report to tell you what you need to know? Could you be relying on it too much? https://riskinsights.com.au/blog/reliable-audits About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in …

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0. Introduction 1:41

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A brief intro to the podcast. If you have suggestions for topics you'd like me to cover, feel free to reach out to me via email. yusuf@riskinsights.com.au 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 (risk…

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Podcast Algorithm Integrity

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Black Mirror Season 7 28:30

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

Risk Insights: Yusuf Moolla

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Article 23. Algorithmic System Integrity: Testing 5:47

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Article 22. Algorithm Integrity: Third party assurance 7:26

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Guest 3. Shea Brown, Founder and CEO of BABL AI 41:23

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Article 21. AI Risk Training: Role-based tailoring 6:26

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Guest 2. Patrick Sullivan: VP of Strategy and Innovation at A-LIGN 32:03

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Guest 1. Ryan Carrier: Executive Director of ForHumanity 44:49

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Article 20. Algorithm Reviews: Public vs Private Reports 8:26

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Article 19. Algorithmic System Reviews: Substantive vs. Controls Testing 6:26

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Article 18. Algorithm Integrity: Training and Awareness 4:07

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Article 17. Algorithm Integrity: Audit vs Review 9:10

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Article 16. Algorithmic System Accuracy Reviews – Choosing the Right Approach 8:07

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Article 15. Algorithm Integrity Documentation - Getting Started 5:19

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Article 14. External data - use with care 6:59

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Article 13. Bridging the purpose-risk gap: Customer-first algorithmic risk assessments 7:18

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Article 12. Risk-Focused Principles for Change Control in Algorithmic Systems 12:00

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Article 11. Deprovisioning User Access to Maintain Algorithm Integrity 9:27

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Article 10. Fairness reviews: identifying essential attributes 6:54

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Article 9. Algorithmic Integrity: Don't wait for legislation 10:39

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Article 8. A Balanced Focus on New and Established Algorithms 8:50

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Article 7. Postcodes: Hidden Proxies for Protected Attributes 11:31

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Article 6. Balancing Security and Access for increased algorithmic integrity 5:41

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Article 5. Equal vs Equitable: Algorithmic Fairness 13:53

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Article 4. Structuring the Audit Objective: 10 Key Aspects of Algorithm Integrity 12:56

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Article 3. Navigate Algorithm Audit Guidance: some aren't relevant to your context 8:55

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Article 2. Choice vs obligation: motivation shapes the effectiveness of your review 6:53

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Article 1. How reliable is the algorithm review that you have commissioned? 10:14

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0. Introduction 1:41

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