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Igor Jablokov, Pryon: Building a responsible AI future

AI News

The current incarnation of Pryon has aimed to confront AI’s ethical quandaries through responsible design focused on critical infrastructure and high-stakes use cases. “[We We wanted to] create something purposely hardened for more critical infrastructure, essential workers, and more serious pursuits,” Jablokov explained.

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With Generative AI Advances, The Time to Tackle Responsible AI Is Now

Unite.AI

Today, seven in 10 companies are experimenting with generative AI, meaning that the number of AI models in production will skyrocket over the coming years. As a result, industry discussions around responsible AI have taken on greater urgency.

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Don’t pause AI development, prioritize ethics instead

IBM Journey to AI blog

Therefore, for both the systems in use today and the systems coming online tomorrow, training must be part of a responsible approach to building AI. We don’t need a pause to prioritize responsible AI. The stakes are simply too high, and our society deserves nothing less.

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Responsible AI at Google Research: The Impact Lab

Google Research AI blog

The Impact Lab team, part of Google’s Responsible AI Team , employs a range of interdisciplinary methodologies to ensure critical and rich analysis of the potential implications of technology development. We examine systemic social issues and generate useful artifacts for responsible AI development.

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5 key areas for governments to responsibly deploy generative AI

IBM Journey to AI blog

To develop responsible AI, government leaders must carefully prepare their internal data to harness the full potential of both AI and generative AI. Setting responsible standards is a crucial government role, requiring the integration of responsibility from the start, rather than as an afterthought.

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The Black Box Problem in LLMs: Challenges and Emerging Solutions

Unite.AI

SHAP's strength lies in its consistency and ability to provide a global perspective – it not only explains individual predictions but also gives insights into the model as a whole. This method requires fewer resources at test time and has been shown to effectively explain model predictions, even in LLMs with billions of parameters.

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AI vs Humans: Stay Relevant or Face the Music

Unite.AI

Likewise, ethical considerations, including bias in AI algorithms and transparency in decision-making, demand multifaceted solutions to ensure fairness and accountability. Addressing bias requires diversifying AI development teams, integrating ethics into algorithmic design, and promoting awareness of bias mitigation strategies.

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