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Article 27. Algorithmic System Integrity: Explainability (Part 4)
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.
To subscribe to the weekly articles: https://riskinsights.com.au/blog#subscribe
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).
View all episodes
By Risk Insights: Yusuf MoollaSpoken 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.
To subscribe to the weekly articles: https://riskinsights.com.au/blog#subscribe
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).