Remove Explainability Remove Explainable AI Remove LLM
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How Large Language Models Are Unveiling the Mystery of ‘Blackbox’ AI

Unite.AI

Thats why explainability is such a key issue. People want to know how AI systems work, why they make certain decisions, and what data they use. The more we can explain AI, the easier it is to trust and use it. Large Language Models (LLMs) are changing how we interact with AI. Thats where LLMs come in.

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Generative AI in the Healthcare Industry Needs a Dose of Explainability

Unite.AI

Increasingly though, large datasets and the muddled pathways by which AI models generate their outputs are obscuring the explainability that hospitals and healthcare providers require to trace and prevent potential inaccuracies. In this context, explainability refers to the ability to understand any given LLM’s logic pathways.

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With Generative AI Advances, The Time to Tackle Responsible AI Is Now

Unite.AI

Promote AI transparency and explainability: AI transparency means it is easy to understand how AI models work and make decisions. Explainability means these decisions can be easily communicated to others in non-technical terms.

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Pace of innovation in AI is fierce – but is ethics able to keep up?

AI News

Indeed, as Anthropic prompt engineer Alex Albert pointed out, during the testing phase of Claude 3 Opus, the most potent LLM (large language model) variant, the model exhibited signs of awareness that it was being evaluated. The second apes the BBC; AI decisions that affect people should not be made without a human arbiter.

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ImandraX: A Breakthrough in Neurosymbolic AI Reasoning and Automated Logical Verification

Unite.AI

As AI systems increasingly power mission-critical applications across industries such as finance, defense, healthcare, and autonomous systems, the demand for trustworthy, explainable, and mathematically rigorous reasoning has never been higher. Raising the Bar in AI Reasoning Denis Ignatovich, Co-founder and Co-CEO of Imandra Inc.,

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Journey to AI blog

Foundation models are widely used for ML tasks like classification and entity extraction, as well as generative AI tasks such as translation, summarization and creating realistic content. The development and use of these models explain the enormous amount of recent AI breakthroughs. Increase trust in AI outcomes.

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12 Can’t-Miss Hands-on Training & Workshops Coming to ODSC East 2025

ODSC - Open Data Science

Perfect for developers and data scientists looking to push the boundaries of AI-powered assistants. Walk away with practical approaches to designing robust evaluation frameworks that ensure AI systems are measurable, reliable, and deployment-ready.