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Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing
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Article 5. Equal vs Equitable: Algorithmic Fairness
Manage episode 439176132 series 3594717
Вміст надано Risk Insights: Yusuf Moolla. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Risk Insights: Yusuf Moolla або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.
Spoken (by a human) version of this article.
Fairness in algorithmic systems is a multi-faceted, and developing, topic.
In episode 4, we explored ten key aspects to consider when scoping an algorithm integrity audit.
One aspect was fairness, with this in the description: "...The design ensures equitable treatment..."
This raises an important question. Shouldn't we aim for equal, rather than equitable treatment?
This episode aims to shed light on the distinctions between equal and equitable treatment in algorithmic systems, while acknowledging that our understanding of fairness is still developing and subject to ongoing debate.
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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