))
Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing
«
»
Article 24. Algorithmic System Integrity: Explainability (Part 1)
Manage episode 525014990 series 3594717
Content provided by Risk Insights: Yusuf Moolla. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Risk Insights: Yusuf Moolla or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
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.
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).
31 episodes
Share
