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Generative AI in the Healthcare Industry Needs a Dose of Explainability

Unite.AI

Increasingly though, large datasets and the muddled pathways by which AI models generate their outputs are obscuring the explainability that hospitals and healthcare providers require to trace and prevent potential inaccuracies. In this context, explainability refers to the ability to understand any given LLM’s logic pathways.

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Global executives and AI strategy for HR: How to tackle bias in algorithmic AI

IBM Journey to AI blog

In line with this trend, the New York City Council has enacted new regulations requiring organizations to conduct yearly bias audits on automated employment decision-making tools used by HR departments. As per the new law, noncompliant organizations may face fines ranging from no less than USD 500 to no more than USD 1500 for each violation.

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Everything You Need to Know About NYC’s Automated Employment Decision Tools (AEDT) Law

Dlabs.ai

In an era where AI and machine learning have streamlined everything, including hiring processes, the balance between efficiency and equity comes into question. With algorithms, machine learning, and statistical modeling defining who gets hired or promoted, are these decisions genuinely fair? What are AEDTs?

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3 key reasons why your organization needs Responsible AI

IBM Journey to AI blog

For many of today’s organizations today, governing AI requires a lot of manual work that include the use of multiple tools, applications and platforms. Lack of automation can lead to lengthy model approval, validation and deployment cycles during which model drift and bias can happen. Read the e-book or visit the website.

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Using AI for Predictive Analytics in Aviation Safety

Aiiot Talk

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. This information serves as a baseline for comparison so the algorithm can identify unusual activity.

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The importance of diversity in AI isn’t opinion, it’s math

IBM Journey to AI blog

Yet many AI creators are currently facing backlash for the biases, inaccuracies and problematic data practices being exposed in their models. These issues require more than a technical, algorithmic or AI-based solution. Consider, for example, who benefits most from content-recommendation algorithms and search engine algorithms.

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Cybersecurity AI Trends to Watch in 2024

Unite.AI

Ultimately, staying updated empowers enthusiasts to leverage the full potential of AI and make confident decisions in their professional and personal pursuits. AI-Powered Threat Detection and Response AI takes the lead in making the digital world safer.

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