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The Importance of Implementing Explainable AI in Healthcare

ODSC - Open Data Science

Healthcare systems are implementing AI, and patients and clinicians want to know how it works in detail. Explainable AI might be the solution everyone needs to develop a healthier, more trusting relationship with technology while expediting essential medical care in a highly demanding world. What Is Explainable AI?

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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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When AI Poisons AI: The Risks of Building AI on AI-Generated Contents

Unite.AI

As generative AI technology advances, there's been a significant increase in AI-generated content. This content often fills the gap when data is scarce or diversifies the training material for AI models, sometimes without full recognition of its implications.

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

IBM Journey to AI blog

It encompasses risk management and regulatory compliance and guides how AI is managed within an organization. Foundation models: The power of curated datasets Foundation models , also known as “transformers,” are modern, large-scale AI models trained on large amounts of raw, unlabeled data.

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Cybersecurity AI Trends to Watch in 2024

Unite.AI

The introduction of generative AI tools marks a shift in disaster recovery processes. The need for explainability in AI algorithms becomes important in meeting compliance requirements. Organizations must showcase how AI-driven decisions are made, making explainable AI models important.

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4 Key Risks of Implementing AI: Real-Life Examples & Solutions

Dlabs.ai

This problem becomes particularly pronounced when employees are unsure why an AI tool makes specific recommendations or decisions and could lead to reluctance to implement the AI’s suggestions. Possible solution: Explainable AI Fortunately, a promising solution exists in the form of Explainable AI.