Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing
Risk Insights: Yusuf Moolla
27 episodes
7 months ago
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 chang...
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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, security, privacy, and performance.Continuous testing is needed for AI systems, differing from traditional testing due to the way these newer systems chang...
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 this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI. Hosted by Yu...
Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing
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 chang...
