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Explainable AI: Demystifying the Black Box Models

Analytics Vidhya

Explainable AI aims to make machine learning models more transparent to clients, patients, or loan applicants, helping build trust and social acceptance of these systems. Now, different models require different explanation methods, depending on the audience.

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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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Explainable AI using OmniXAI

Analytics Vidhya

Introduction In the modern day, where there is a colossal amount of data at our disposal, using ML models to make decisions has become crucial in sectors like healthcare, finance, marketing, etc. Many ML models are black boxes since it is difficult to […].

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Explainable AI Using Expressive Boolean Formulas

Unite.AI

And if these applications are not expressive enough to meet explainability requirements, they may be rendered useless regardless of their overall efficacy. Based on our findings, we have determined that Explainable AI using expressive Boolean formulas is both appropriate and desirable for those use cases that mandate further explainability.

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The Hidden Risks of DeepSeek R1: How Large Language Models Are Evolving to Reason Beyond Human Understanding

Unite.AI

This success, however, has come at a cost, one that could have serious implications for the future of AI development. The Language Challenge DeepSeek R1 has introduced a novel training method which instead of explaining its reasoning in a way humans can understand, reward the models solely for providing correct answers.

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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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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.