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Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing
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Article 7. Postcodes: Hidden Proxies for Protected Attributes
Manage episode 441658125 series 3594717
المحتوى المقدم من Risk Insights: Yusuf Moolla. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Risk Insights: Yusuf Moolla أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.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).
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