Spoken by a human version of this article.

Ongoing education helps everyone understand their role in responsibly developing and using algorithmic systems.

Regulators and standard-setting bodies emphasise the need for AI literacy across all organisational levels.

Links

• ForHumanity - join the growing community here.
• ForHumanity - free courses here.
• IAIS: The International Association of Insurance Supervisors is developing a guidance paper on the supervision of AI.
• DNB: De Nederlandsche Bank - 6 general principles for the use of AI in the financial sector.
• ASIC: The Australian Securities & Investments Commission - report.
• NIST: The National Institute of Standards and Technology - AI Risk Management Framework.
• EU AI Act: The European Union Artificial Intelligence Act - specific expectation about “AI literacy”.

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).

Spoken by a human version of this article.

The terminology – “audit” vs “review” - is important, but clarity about deliverables is more important when commissioning algorithm integrity assessments.

Audits are formal, with an opinion or conclusion that can often be shared externally. Reviews come in various forms and typically produce recommendations, for internal use.

Regardless of the terminology you use, when commissioning an assessment, clearly define and document the expected deliverable, including the report content and intended distribution, to ensure expectations are met.

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).

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).