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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
محتوای ارائه شده توسط Risk Insights: Yusuf Moolla. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Risk Insights: Yusuf Moolla یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.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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