Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

About Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

Insights for financial services leaders who want to enhance fairness and accuracy in their use of data, algorithms, and AI. Each episode explores challenges and solutions related to algorithmic integrity, including discussions on navigating independent audits. The goal of this podcast is to give leaders the knowledge they need to ensure their data practices benefit customers and other stakeholders, reducing the potential for harm and upholding industry standards.

Publisher

Risk Insights: Yusuf Moolla

Category

business · business

Language

en

Explicit

No

First episode

6 Aug 2024

Latest episode

22 Dec 2025

Latest episodes

33 episodes in the feed.

1. 

   22 Dec 2025

   Article 29. Algorithmic System Integrity: Explainability (Part 6) - Interpretability

   Spoken by a human version of this article. TL;DR (TL;DL?) Technical stakeholders need detailed explanations.Non-technical stakeholders need plain language.Visuals, layering, literacy, and feedback are among the techniques we can use.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).

2. 

   21 Dec 2025

   Article 28. Algorithmic System Integrity: Explainability (Part 5) - Privacy and Confidentiality

   Send us a text Spoken by a human version of this article. TL;DR (TL;DL?) Algorithmic systems create challenges in balancing explainability with privacy and confidentiality.Key challenges include protecting sensitive information, preserving proprietary algorithms, and securing fraud detection systems.Focusing on what audiences need, with a few specific considerations, can help address these.About this podcast A podcast for Financial Services leaders, where we discuss fairness and accuracy in ...

3. 

   20 Dec 2025

   Article 27. Algorithmic System Integrity: Explainability (Part 4)

   Send us a text Spoken by a human version of this article. TL;DR (TL;DL?) Explainability is necessary to build trust in AI systems.There is no universally accepted definition of explainability.So we focus on key considerations that don't require us to select any particular definition.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.a...

Podcast Authority Score: 38 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

76

Social presence

0

YouTube

0

Engagement

0