Explainable, Interpretable, and Transparent AI Systems

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(as of Dec 27,2024 02:18:44 UTC – Details)

ASIN ‏ : ‎ B0D8L4Z312
Publisher ‏ : ‎ CRC Press; 1st edition (August 23, 2024)
Publication date ‏ : ‎ August 23, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 34991 KB
Simultaneous device usage ‏ : ‎ Up to 4 simultaneous devices, per publisher limits
Text-to-Speech ‏ : ‎ Not enabled
Enhanced typesetting ‏ : ‎ Not Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Format ‏ : ‎ Print Replica

Explainable, Interpretable, and Transparent AI Systems: Why They Matter

In recent years, artificial intelligence (AI) has made significant advancements in various fields, from healthcare to finance to transportation. However, as AI systems become more complex and integrated into our daily lives, there is a growing need for these systems to be explainable, interpretable, and transparent.

Explainable AI refers to the ability of AI systems to provide clear explanations for their decisions and actions. This is crucial for building trust and understanding among users, especially in high-stakes applications such as healthcare and criminal justice. Interpretable AI goes a step further, allowing users to understand how the AI system arrived at its decisions and predictions. This can help identify biases, errors, and limitations in the system, as well as improve its overall performance.

Transparency in AI systems involves making the system’s inner workings and data sources accessible to users and stakeholders. This transparency is essential for accountability, compliance with regulations such as GDPR, and ensuring that AI systems are fair and ethical.

Overall, explainable, interpretable, and transparent AI systems are essential for building trust, ensuring accountability, and promoting ethical AI development. As AI continues to play a larger role in society, it is crucial that we prioritize these principles to create AI systems that benefit everyone.
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