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).

Spoken by a human version of this article.

One question that comes up often is “How do we obtain assurance about third party products or services?”

Depending on the nature of the relationship, and what you need assurance for, this can vary widely.

This article attempts to lay out the options, considerations, and key steps to take.

TL;DR (TL;DL?)

• Third-party assurance for algorithm integrity varies based on the nature of the relationship and specific needs, with several options.
• Key factors to consider include the importance and risk level of the service/product, regulatory expectations, complexity, transparency, and frequency of updates.
• Standardised assurance frameworks for algorithm integrity are still emerging; adopt a risk-based approach, and consider sector-specific standards like CPS230(Australia).

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).

Navigating AI Audits with Dr. Shea Brown

Dr. Shea Brown is Founder and CEO of BABL AI
BABL specializes in auditing and certifying AI systems, consulting on responsible AI practices, and offering online education.

Shea shares his journey from astrophysics to AI auditing, the core services provided by BABL AI including compliance audits, technical testing, and risk assessments, and the importance of governance in AI.

He also addresses the challenges posed by generative AI, the need for continuous upskilling in AI literacy, and the role of organizations like the IAAA and For Humanity in building consensus and standards in AI auditing.

Finally, Shea provides insights on third-party risks, in-house AI developments, and key skills needed for effective AI governance.

Chapter Markers

00:00 Introduction to Dr. Shea Brown and BABL AI

00:36 The Journey from Astrophysics to AI Auditing

02:22 Core Services and Compliance Audits at BABL

03:57 Educational Initiatives and AI Literacy

05:48 Collaborations and Professional Organizations

08:57 Approach to AI Audits and Readiness

17:29 Challenges with Generative AI in Audits

29:21 Trends in AI Deployment and Risk Assessment

34:53 Skills and Training for AI Governance

40:15 Conclusion and Contact Information

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).