Remove AI Development Remove AI Tools Remove Explainability
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Generative AI in the Healthcare Industry Needs a Dose of Explainability

Unite.AI

The remarkable speed at which text-based generative AI tools can complete high-level writing and communication tasks has struck a chord with companies and consumers alike. In this context, explainability refers to the ability to understand any given LLM’s logic pathways.

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Advancing AI trust with new responsible AI tools, capabilities, and resources

AWS Machine Learning Blog

Responsible AI builds trust, and trust accelerates adoption and innovation. Technical standards, such as ISO/IEC 42001, are significant because they provide a common framework for responsible AI development and deployment, fostering trust and interoperability in an increasingly global and AI-driven technological landscape.

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AI and Financial Crime Prevention: Why Banks Need a Balanced Approach

Unite.AI

Humans can validate automated decisions by, for example, interpreting the reasoning behind a flagged transaction, making it explainable and defensible to regulators. Financial institutions are also under increasing pressure to use Explainable AI (XAI) tools to make AI-driven decisions understandable to regulators and auditors.

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Transparency in AI: How Tülu 3 Challenges the Dominance of Closed-Source Models

Unite.AI

The Importance of Transparency in AI Transparency is essential for ethical AI development. Without it, users must rely on AI systems without understanding how decisions are made. Transparency allows AI decisions to be explained, understood, and verified. This is particularly important in areas like hiring.

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Western Bias in AI: Why Global Perspectives Are Missing

Unite.AI

A 2023 report by the AI Now Institute highlighted the concentration of AI development and power in Western nations, particularly the United States and Europe, where major tech companies dominate the field. Economically, neglecting global diversity in AI development can limit innovation and reduce market opportunities.

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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. Not necessarily.

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CES 2025: AI Advancing at ‘Incredible Pace,’ NVIDIA CEO Says

NVIDIA

Then generative AI creating text, images and sound, Huang said. Now, were entering the era of physical AI, AI that can proceed, reason, plan and act. The latest generation of DLSS can generate three additional frames for every frame we calculate, Huang explained. The next frontier of AI is physical AI, Huang explained.

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