Remove Algorithm Remove Black Box AI Remove Explainability
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AI for Money Managers: Avoid the Black Box – And Do This Instead

Unite.AI

The opportunities afforded by AI are truly significant – but can we trust black box AI to produce the right results? Instead of utilizing AI systems that they cannot explainblack box AI systems – they could utilize AI platforms that use transparent techniques , explaining how they arrive at their conclusions.

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Who Is Responsible If Healthcare AI Fails?

Unite.AI

Similarly, what if a drug diagnosis algorithm recommends the wrong medication for a patient and they suffer a negative side effect? At the root of AI mistakes like these is the nature of AI models themselves. Most AI today use “black box” logic, meaning no one can see how the algorithm makes decisions.

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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

Bias and Inequality AI can also introduce societal issues like exaggerating bias if corporations aren’t careful. Amazon’s scrapped hiring AI model infamously penalized women’s resumes as the machine learning algorithm expanded on implicit biases within the training data. Thankfully, the world is moving in this direction.