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Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing
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Article 16. Algorithmic System Accuracy Reviews – Choosing the Right Approach
Manage episode 453207773 series 3594717
A tartalmat a Risk Insights: Yusuf Moolla biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Risk Insights: Yusuf Moolla vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
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 provide deeper insights into system reliability and accuracy.
This article explains why verifying outcomes is preferred over tracing through processes, and how it works.
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).
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