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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 existing and emerging 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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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 However: existing legislation may already apply new algorithm-focused laws can be too narrow or quickly outdated standards ca…
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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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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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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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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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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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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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Spoken (by a human) version of this article. The motivation(s) for commissioning an audit 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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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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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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