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How Large Language Models Are Unveiling the Mystery of ‘Blackbox’ AI

Unite.AI

AI is becoming a more significant part of our lives every day. But as powerful as it is, many AI systems still work like black boxes. 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. We dont need to be an AI expert to use it.

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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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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

The explosion in artificial intelligence (AI) and machine learning applications is permeating nearly every industry and slice of life. While AI exists to simplify and/or accelerate decision-making or workflows, the methodology for doing so is often extremely complex. But its growth does not come without irony.

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

Unite.AI

AI is a two-sided coin for banks: while its unlocking many possibilities for more efficient operations, it can also pose external and internal risks. In the US alone, generative AI is expected to accelerate fraud losses to an annual growth rate of 32%, reaching US$40 billion by 2027, according to a recent report by Deloitte.

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Navigating AI Bias: A Guide for Responsible Development

Unite.AI

AI is revolutionizing industries worldwide, but with this transformation comes significant responsibility. The consequences of unchecked AI can be severe, from legal penalties to reputational damage but no company is doomed. AI Bias Risks Companies Face AI is transforming industries, but as mentioned, it comes with significant risks.

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Data Monocultures in AI: Threats to Diversity and Innovation

Unite.AI

AI is reshaping the world, from transforming healthcare to reforming education. Data is at the centre of this revolutionthe fuel that powers every AI model. In AI, relying on uniform datasets creates rigid, biased, and often unreliable models. AI systems can also become fragile when trained on limited data.

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