Unite.AI

MARCH 14, 2024

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.

Black Box AI 299

Black Box AI Explainability Algorithm AI 299

Unite.AI

JUNE 26, 2023

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.

Black Box AI 297

Black Box AI Explainable AI Explainability AI 297

Towards AI

NOVEMBER 7, 2024

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.

Black Box AI 100

Black Box AI AI AI Machine Learning 100

Unite.AI

APRIL 9, 2024

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.

Composite AI 182

Composite AI Explainability Black Box AI Explainable AI 182

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.

Black Box AI 130

Black Box AI Algorithm Explainable AI Explainability 130

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.

Neural Network 112

Neural Network Black Box AI AI Modeling Explainability 112

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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Black Box AI Data Science AI AI 98