Remove AI Development Remove Algorithm Remove Black Box AI Remove Explainability
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Who Is Responsible If Healthcare AI Fails?

Unite.AI

Who is responsible when AI mistakes in healthcare cause accidents, injuries or worse? Depending on the situation, it could be the AI developer, a healthcare professional or even the patient. Liability is an increasingly complex and serious concern as AI becomes more common in healthcare. AI Gone Wrong: Who’s to Blame?

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Enhancing AI Transparency and Trust with Composite AI

Unite.AI

The adoption of Artificial Intelligence (AI) has increased rapidly across domains such as healthcare, finance, and legal systems. However, this surge in AI usage has raised concerns about transparency and accountability. Composite AI is a cutting-edge approach to holistically tackling complex business problems.

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Using AI for Predictive Analytics in Aviation Safety

Aiiot Talk

AI is today’s most advanced form of predictive maintenance, using algorithms to automate performance and sensor data analysis. Aircraft owners or technicians set up the algorithm with airplane data, including its key systems and typical performance metrics. Black-box AI poses a serious concern in the aviation industry.

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Unlocking the Black Box: LIME and SHAP in the Realm of Explainable AI

Mlearning.ai

Principles of Explainable AI( Source ) Imagine a world where artificial intelligence (AI) not only makes decisions but also explains them as clearly as a human expert. This isn’t a scene from a sci-fi movie; it’s the emerging reality of Explainable AI (XAI). What is Explainable AI?

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Is Rapid AI Adoption Posing Serious Risks for Corporations?

ODSC - Open Data Science

This is a promising shift for AI developers, and many organizations have realized impressive benefits from the technology, but it also comes with significant risks. AI’s rapid growth could lead more companies to implement it without fully understanding how to manage it safely and ethically.