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Machine unlearning: Researchers make AI models ‘forget’ data

AI News

While some existing methods already cater to this need, they tend to assume a white-box approach where users have access to a models internal architecture and parameters. Black-box AI systems, more common due to commercial and ethical restrictions, conceal their inner mechanisms, rendering traditional forgetting techniques impractical.

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

Unite.AI

Theyre making AI explanations accessible to everyone, not just tech professionals. This method is designed to simplify complex explanations of explainable AI algorithms, making it easier for people from all backgrounds to understand. These agents are designed to make interacting with AI feel more like conversing.

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Google’s Gemini transparency cut leaves enterprise developers ‘debugging blind’

Flipboard

The feature is especially important for creating agentic workflows, where the AI must execute a series of tasks. Black-box AI models that hide their reasoning introduce significant risk, making it difficult to trust their outputs in high-stakes scenarios. For enterprises, this move toward opacity can be problematic.

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What is Responsible AI

Pickl AI

Understanding the consequences of AI misuse and the challenges of unregulated systems is essential to realising its benefits without harm. Examples of AI Misuse and Consequences AI misuse has led to notable failures with far-reaching impacts. This unregulated environment fosters misuse and amplifies risks.

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Yubei Chen, Co-Founder of Aizip Inc – Interview Series

Unite.AI

My PhD research, which combined electrical engineering, computer science, and computational neuroscience, focused on understanding AI systems from a “white-box” perspective, or developing methods to reveal the underlying structures of data and learning models.

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AI for Money Managers: Avoid the Black Box – And Do This Instead

Unite.AI

The opportunities afforded by AI are truly significant – but can we trust black box AI to produce the right results? Instead of utilizing AI systems that they cannot explain – black box AI systems – they could utilize AI platforms that use transparent techniques , explaining how they arrive at their conclusions.

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Who Is Responsible If Healthcare AI Fails?

Unite.AI

Similarly, what if a drug diagnosis algorithm recommends the wrong medication for a patient and they suffer a negative side effect? 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.