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Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing
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Article 23. Algorithmic System Integrity: Testing
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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).
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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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