Unite.AI

MARCH 18, 2025

Certain large companies have control over a vast amount of data, which creates an uneven playing field wherein only a select few have access to information necessary to train AI models and drive innovation. Public web data should remain accessible to businesses, researchers, and developers. This is not how things should be.

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ODSC - Open Data Science

OCTOBER 21, 2024

As AI systems become increasingly embedded in critical decision-making processes and in domains that are governed by a web of complex regulatory requirements, the need for responsible AI practices has never been more urgent. But let’s first take a look at some of the tools for ML evaluation that are popular for responsible AI.

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IBM Journey to AI blog

OCTOBER 16, 2023

. “Foundation models make deploying AI significantly more scalable, affordable and efficient.” It’s essential for an enterprise to work with responsible, transparent and explainable AI, which can be challenging to come by in these early days of the technology. ” Are foundation models trustworthy?

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Towards AI

OCTOBER 19, 2024

Yet, for all their sophistication, they often can’t explain their choices — this lack of transparency isn’t just frustrating — it’s increasingly problematic as AI becomes more integrated into critical areas of our lives. What is Explainability AI (XAI)?

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Viso.ai

FEBRUARY 12, 2024

True to its name, Explainable AI refers to the tools and methods that explain AI systems and how they arrive at a certain output. Artificial Intelligence (AI) models assist across various domains, from regression-based forecasting models to complex object detection algorithms in deep learning.

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NVIDIA

MARCH 1, 2024

Trustworthy AI initiatives recognize the real-world effects that AI can have on people and society, and aim to channel that power responsibly for positive change. What Is Trustworthy AI? Trustworthy AI is an approach to AI development that prioritizes safety and transparency for those who interact with it.

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Unite.AI

JANUARY 12, 2024

Competitions also continue heating up between companies like Google, Meta, Anthropic and Cohere vying to push boundaries in responsible AI development. The Evolution of AI Research As capabilities have grown, research trends and priorities have also shifted, often corresponding with technological milestones.

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Marktechpost

DECEMBER 11, 2024

Image Source : LG AI Research Blog ([link] Responsible AI Development: Ethical and Transparent Practices The development of EXAONE 3.5 models adhered to LG AI Research s Responsible AI Development Framework, prioritizing data governance, ethical considerations, and risk management.

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ODSC - Open Data Science

FEBRUARY 25, 2025

Large Language Models & RAG TrackMaster LLMs & Retrieval-Augmented Generation Large language models (LLMs) and retrieval-augmented generation (RAG) have become foundational to AI development. AI Engineering TrackBuild Scalable AISystems Learn how to bridge the gap between AI development and software engineering.

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Towards AI

OCTOBER 7, 2023

Last Updated on October 9, 2023 by Editorial Team Author(s): Lye Jia Jun Originally published on Towards AI. Balancing Ethics and Innovation: An Introduction to the Guiding Principles of Responsible AI Sarah, a seasoned AI developer, found herself at a moral crossroads.

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ODSC - Open Data Science

FEBRUARY 18, 2025

AI Ethicists: As AI systems become more integrated into society, ethical considerations are paramount. AI ethicists specialize in ensuring that AI development and deployment align with ethical guidelines and regulatory standards, preventing unintended harm andbias.

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AI News

APRIL 25, 2024

Pryon also emphasises explainable AI and verifiable attribution of knowledge sources. Ensuring responsible AI development Jablokov strongly advocates for new regulatory frameworks to ensure responsible AI development and deployment.

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Pickl AI

DECEMBER 29, 2024

Summary: Responsible AI ensures AI systems operate ethically, transparently, and accountably, addressing bias and societal risks. Through ethical guidelines, robust governance, and interdisciplinary collaboration, organisations can harness AI’s transformative power while safeguarding fairness and inclusivity.

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IBM Journey to AI blog

FEBRUARY 28, 2024

IBM watsonx™ , an integrated AI, data and governance platform, embodies five fundamental pillars to help ensure trustworthy AI: fairness, privacy, explainability, transparency and robustness. This platform offers a seamless, efficient and responsible approach to AI development across various environments.

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Pickl AI

DECEMBER 10, 2024

As the global AI market, valued at $196.63 from 2024 to 2030, implementing trustworthy AI is imperative. This blog explores how AI TRiSM ensures responsible AI adoption. Key Takeaways AI TRiSM embeds fairness, transparency, and accountability in AI systems, ensuring ethical decision-making.

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