AI News

DECEMBER 10, 2024

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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Aiiot Talk

JULY 7, 2023

AI can streamline and automate key safety processes such as design, monitoring, testing and more. AI-Powered Predictive Maintenance AI is a powerful tool for improving aircraft safety through predictive analytics. Black-box AI poses a serious concern in the aviation industry.

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DataRobot Blog

APRIL 29, 2022

The stakes in managing model risk are at an all-time high, but luckily automated machine learning provides an effective way to reduce these risks. 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. Download now.

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ODSC - Open Data Science

APRIL 4, 2023

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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LevelAI

AUGUST 9, 2023

Auto-QA Today Contact center auto-QA (Quality Assurance) refers to the use of automated tools and technologies to assess and evaluate the quality of interactions between contact center agents and customers. Say goodbye to black-box AI models where you’re never quite sure if the AI got it right.

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DataRobot Blog

NOVEMBER 3, 2022

With automated feature engineering, automated model development, and more explainable forecasts, data scientists can build more models with more accuracy, speed, and confidence. We want to avoid that “black box AI” where it’s unclear why certain decisions were made. Getting forecasts right matters.

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Pickl AI

DECEMBER 29, 2024

Challenges in Unregulated AI Systems Unregulated AI systems operate without ethical boundaries, often resulting in biased outcomes, data breaches, and manipulation. The lack of transparency in AI decision-making (“black-box AI”) makes accountability difficult.

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