Aiiot Talk

JULY 7, 2023

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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Towards AI

DECEMBER 19, 2023

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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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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DataRobot Blog

APRIL 29, 2022

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?

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LevelAI

AUGUST 9, 2023

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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O'Reilly Media

DECEMBER 12, 2023

The problem with Lanier’s concept of data dignity is that, given the current state of the art in AI models, it is impossible to distinguish meaningfully between “training” and “generating output.” He asks, “Why don’t bits come attached to the stories of their origins?

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Mlearning.ai

DECEMBER 31, 2023

Unlike traditional ‘black box’ AI models that offer little insight into their inner workings, XAI seeks to open up these black boxes, enabling users to comprehend, trust, and effectively manage AI systems.

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DataRobot Blog

NOVEMBER 3, 2022

We want to avoid that “black box AI” where it’s unclear why certain decisions were made. As experienced data scientists, we understand that modeling is only part of our work. But if we can’t communicate insights to others, our models aren’t as useful as they could be. It’s also important to be able to trust the model.

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Artificial Corner

JUNE 21, 2023

However, imposing strict regulations on AI could clash with the principles of open science and hinder the flow of information that is critical for progress. Unveiling the Black Box One of the primary challenges in regulating AI lies in the inherent opacity of its algorithms.

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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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