responsible-ai

The Lenfest Institute expands its AI Collaborative and Fellowship Program with a $5 million commitment from OpenAI, aiming to embed AI engineers in news organizations for innovation and responsible AI adoption in journalism.

This paper proposes using literary interpretation as a setting to evaluate cultural reasoning depth in language models, emphasizing interpretive depth and evidence-centered benchmarks over conventional metrics.

OpenAI is collaborating with an independent advisory group of mathematicians to guide the responsible review and communication of emerging AI results in mathematics, ensuring academic standards and public transparency.

OpenAI forms an independent advisory group of mathematicians to guide responsible sharing of AI advances in mathematics, following a new model that resolved over 100 open math problems.

AI leaders including Demis Hassabis and Jensen Huang met with King Charles to discuss responsible AI safety, expressing optimism and the need for collective action to address risks.

OpenAI has released the Australian Youth Safety Blueprint, a roadmap outlining six pillars to safeguard young users in AI interactions, focusing on literacy, privacy, and company accountability.

This study audits CLIP models for gender bias in Metropolitan Museum artwork metadata, finding no statistically significant bias but emphasizing the need for multivariate confound control in AI fairness assessments.

California aims to remain a leader in responsible AI innovation, urging AI companies to mitigate risks and collaborate with state authorities. SV Angel's Project Blueprint is praised for promoting shared prosperity and consensus among industry leaders and lawmakers.

Sam Altman emphasizes the need for AI companies to act responsibly and welcomes a federal framework for frontier AI safety, advocating for shared standards and collaboration.

Microsoft announced a new agreement with the American Federation of Teachers to establish the National AI Safety & Privacy Standard for Schools, promoting safe and responsible AI use in education.

The Responsible AI Track at PyTorch Conference North America 2026 covers governance, privacy, security, safety, compliance, and ethical AI development within the PyTorch ecosystem.

This paper proposes value-preserving architectural patterns for agentic AI systems, such as privacy-aware, distributed, and guard-agent architectures, to integrate human-centered values like privacy, fairness, and safety into multi-agent system designs.

This paper applies hermeneutic philosophy to large language models to address interpretive risks like 'interpretive misplacement' and derives design principles for responsible human-AI co-interpretation in contexts such as law, education, and public discourse.

Advocates against blindly pasting AI-generated responses and encourages thoughtful human input in communication.

Bill Gates proposes a robot tax and 'Human Reserved' jobs to address AI's social impacts, aiming to slow labor displacement and raise funds for retraining.

This paper presents RIACT, a responsible AI system for personalized study habit tracking and early burnout detection in university students, combining structured logging with AI to provide insights and recommendations.

The article highlights a critical gap between the deployment of AI agents handling sensitive data and the lack of compliance and governance controls, with significant regulatory risks like EU fines, and points to solutions like Lyzr's Responsible AI layer for integrated security.

OpenAI temporarily paused reinforcement learning training on its latest models due to growing risks, and is strengthening monitoring, alignment, and security safeguards for safer AI development.

OpenAI and CodeAI have announced a partnership to provide AI educational tools and resources for students and educators, including the launch of ChatGPT for Teens, which emphasizes critical thinking and responsible use.

ASSERT is a specification-driven measurement pipeline for auditing generative AI systems, which ties reported rates to explicit measurement choices and demonstrates how these choices can affect compliance rates and system rankings.