Remove Black Box AI Remove Explainability Remove Information
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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

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. Black box AI lack transparency, leading to risks like logic bias , discrimination and inaccurate results.

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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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#48 Interpretability Might Not Be What Society Is Looking for in AI

Towards AI

What’s AI Weekly This week in High Learning Rate, my other newsletter, we go back to the basics and explore the popular retrieval-augmented generation (RAG) method, introduced by a Meta paper in 2020. In one line, RAG answers the known limitations of LLMs, such as non-access to up-to-date information and hallucinations.

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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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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. This information serves as a baseline for comparison so the algorithm can identify unusual activity. IoT sensors that detect performance outside expected margins trigger the AI to alert maintenance personnel.

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Is Rapid AI Adoption Posing Serious Risks for Corporations?

ODSC - Open Data Science

Security and Compliance Cybersecurity is one of the biggest risks of rising AI adoption. These models require substantial amounts of data, and many organizations use “black box” models where they’re unsure of how the system uses the information they give it. Thankfully, the world is moving in this direction.