Unite.AI

JUNE 26, 2023

Who is responsible when AI mistakes in healthcare cause accidents, injuries or worse? Depending on the situation, it could be the AI developer, a healthcare professional or even the patient. Liability is an increasingly complex and serious concern as AI becomes more common in healthcare. Not necessarily.

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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.

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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.

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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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ODSC - Open Data Science

APRIL 4, 2023

This is a promising shift for AI developers, and many organizations have realized impressive benefits from the technology, but it also comes with significant risks. AI’s rapid growth could lead more companies to implement it without fully understanding how to manage it safely and ethically.

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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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