The post discusses the development of phi-3-mini in alignment with Microsoft’s responsible AI principles, focusing on safety measures such as post-training safety alignment and red-teaming. It highlights the importance of addressing AI harm categories through curated datasets and iterative improvements based on feedback from an independent red team.
The Scottish AI Alliance has released its 2024/2025 Impact Report, showcasing significant progress in promoting ethical and inclusive artificial intelligence across Scotland. The report highlights various initiatives, including the engagement of over 1,500 learners in the Living with AI course and support for more than 200 businesses through the relaunch of the Scottish AI Playbook.
In today’s competitive landscape, deploying Large Language Models (LLMs) requires a robust LLMOps framework to ensure reliability and compliance. Python’s rich ecosystem serves as a linchpin, integrating prototyping, monitoring, and governance into a seamless production workflow.
The Asia-Pacific region is experiencing a rapid expansion of AI-powered surveillance technologies, especially from Chinese companies, yet lacks the governance frameworks to regulate their use effectively. This creates a significant risk as these technologies can be repurposed to consolidate political control and suppress dissent.
A model card report is a standardized document that provides transparency and accountability in AI model development and deployment. It outlines a model’s purpose, usage, performance metrics, and limitations, making it an essential tool for responsible AI governance amid increasing regulatory demands.
AI models have become more integrated into enterprise workflows, but they face significant security threats from jailbreak attempts that exploit their built-in restrictions. These deliberate efforts to bypass ethical and operational rules highlight vulnerabilities in AI governance, especially when enterprise models cannot consistently refuse harmful requests.
An advocacy group warns that the increasing use of AI in financial services is leading to discrimination and exploitation of consumers, highlighting significant risks such as financial exclusion and mis-selling. They call for urgent reforms to address the gaps in existing regulations to ensure fairness and transparency in AI-driven financial decision-making.
President Donald Trump’s budget reconciliation bill almost included a decade-long moratorium on AI regulation at the state and local levels, but this provision was ultimately removed by the Senate. As a result, states remain free to create their own regulations for AI, highlighting ongoing debates about consumer protection and innovation in the sector.
The rising use of unapproved AI tools, known as shadow AI, poses significant compliance and reputational risks for organizations, particularly in regulated sectors such as finance and healthcare. As employees turn to these tools when sanctioned options are lacking, it is crucial for companies to adopt a proactive approach to AI integration and governance.
The article discusses the importance of governance for AI agents, emphasizing that companies must understand and catalogue the AI tools operating within their environments to ensure responsible use. It highlights the need for visibility and monitoring to prevent potential risks and failures that could disrupt business processes.