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


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?


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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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.

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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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Is Rapid AI Adoption Posing Serious Risks for Corporations?

ODSC - Open Data Science

Transparency The lack of transparency in many AI models can also cause issues. Users may not understand how these systems work and it can be difficult to figure out, especially with black-box AI. Being unable to resolve things could lead businesses to experience significant losses from unreliable AI applications.

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The future of QA is here, meet QA-GPT


In our testing, we found that QA-GPT can cover over 85% of scorecard questions out of the box without any extra configuration. Say goodbye to black-box AI models where you’re never quite sure if the AI got it right. We’re also improving the transparency of evaluations.

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What is Model Risk and Why Does it Matter?

DataRobot Blog

Opening the “ Black Box AI ”: The Path to Deployment of AI Models in Banking What You Need to Know About Model Risk Management. More on this topic. The Framework for ML Governance. Download now. The post What is Model Risk and Why Does it Matter?