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 change (without code changes).

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).