))
Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing
«
»
Article 7. Postcodes: Hidden Proxies for Protected Attributes
Fetch error
Hmmm there seems to be a problem fetching this series right now.
Last successful fetch was on October 30, 2025 20:37 (
What now? This series will be checked again in the next day. If you believe it should be working, please verify the publisher's feed link below is valid and includes actual episode links. You can contact support to request the feed be immediately fetched.
Manage episode 441658125 series 3594717
Kandungan disediakan oleh Risk Insights: Yusuf Moolla. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Risk Insights: Yusuf Moolla atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
Spoken (by a human) version of this article.
In a previous article, we discussed algorithmic fairness, and how seemingly neutral data points can become proxies for protected attributes.
In this article, we'll explore a concrete example of a proxy used in insurance and banking algorithms: postcodes.
We've used Australian terminology and data. But the concept will apply to most countries.
Using Australian Bureau of Statistics (ABS) Census data, it aims to demonstrate how postcodes can serve as hidden proxies for gender, disability status and citizenship.
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).
27 episod
Kongsi
