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Article 24. Algorithmic System Integrity: Explainability (Part 1)
Spoken by a human version of this article.
TL;DR (TL;DL?)
- Why Explainability Matters: It builds trust, is needed to meet compliance obligations, and can help identify errors faster.
- Key Challenges: Complex algorithms, intricate workflows, privacy concerns, and making explanations understandable for all stakeholders.
- What’s Next: Future articles will explore practical solutions to these challenges.
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?)
- Why Explainability Matters: It builds trust, is needed to meet compliance obligations, and can help identify errors faster.
- Key Challenges: Complex algorithms, intricate workflows, privacy concerns, and making explanations understandable for all stakeholders.
- What’s Next: Future articles will explore practical solutions to these challenges.
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).