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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

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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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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Enhancing AI Transparency and Trust with Composite AI

Unite.AI

The adoption of Artificial Intelligence (AI) has increased rapidly across domains such as healthcare, finance, and legal systems. However, this surge in AI usage has raised concerns about transparency and accountability. Composite AI is a cutting-edge approach to holistically tackling complex business problems.

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#48 Interpretability Might Not Be What Society Is Looking for in AI

Towards AI

Building Trustworthy AI: Interpretability in Vision and Linguistic Models By Rohan Vij This article explores the challenges of the AI black box problem and the need for interpretable machine learning in computer vision and large language models.

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How Do Inherently Interpretable AI Models Work? — GAMINET

Towards AI

It is very risky to apply these black-box AI systems in real-life applications, especially in sectors like banking and healthcare. The models are becoming more and more complex with deeper layers leading to greater accuracy. One issue with this current trend is the focus on interpretability is lost at times.

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Using AI for Predictive Analytics in Aviation Safety

Aiiot Talk

When developers and users can’t see how AI connects data points, it is more challenging to notice flawed conclusions. Black-box AI poses a serious concern in the aviation industry. In fact, explainability is a top priority laid out in the European Union Aviation Safety Administration’s first-ever AI roadmap.