The FDA’s recent rollout of the internal AI tool, Elsa, aims to address the challenges of regulatory document review, but experts warn that creating effective regulated AI is highly complex. Erez Kaminski, CEO of Ketryx, suggests that a neuro-symbolic approach, combining neural networks and rule-based AI, may be essential for managing the intricate demands of regulatory environments.
New York state lawmakers have passed a bill requiring developers of advanced AI models to implement comprehensive safety plans to mitigate risks of serious harm. However, federal lawmakers are considering measures that could block these state regulations before they take effect.
AI spending in the Asia-Pacific region has surged, with a notable increase of 3.3 times compared to previous years, yet many CIOs still struggle with governance and compliance policies. The Philippines is focusing on innovation and digital infrastructure to enhance AI integration across various sectors, reflecting a growing commitment to becoming a key player in the regional AI ecosystem.
The European Union’s AI Act, initially praised for its ambitious regulation of artificial intelligence risks, is now facing potential revisions due to political pressures and industry lobbying. Concerns arise that these changes may compromise safety standards as the EU seeks to compete more effectively with the U.S. and China in the AI sector.
As machine learning becomes integral to strategic decision-making, effective model management is essential for ensuring reliability, accountability, and regulatory compliance in AI-driven enterprises. It encompasses practices such as version control, governance, and performance monitoring, making it a critical necessity for long-term success.
The EU AI Act is the world’s first comprehensive AI legislation, establishing a risk-based framework that categorizes AI systems into four risk levels, each with specific compliance requirements. This regulation not only applies to businesses operating within the EU but also to those whose AI outputs impact EU citizens, emphasizing the global reach of AI governance.
As machine learning systems increasingly influence high-stakes decisions in hiring, lending, and criminal justice, the need for rigorous bias detection and mitigation has become paramount. This article presents a complete technical framework for implementing responsible AI practices, demonstrating how to systematically identify, measure, and mitigate algorithmic bias using industry-standard tools and methodologies.
The New York State Senate has passed the RAISE Act, a significant AI safety bill aimed at imposing critical safeguards on the development and deployment of artificial intelligence technologies. Spearheaded by Senator Andrew Gounardes, the legislation seeks to ensure that developers implement safety protocols to protect society as AI continues to permeate various sectors.
The EU AI Act is the first comprehensive legal framework regulating artificial intelligence, ensuring that AI systems in the EU are safe, transparent, and ethical. It classifies AI systems into four risk categories, imposing stricter compliance requirements on high-risk systems used in sensitive areas like healthcare and law enforcement.
Generative artificial intelligence (Gen AI) is transforming the business landscape, offering significant opportunities for growth while also presenting unique risks that organizations must manage. To deploy Gen AI effectively, businesses need to address challenges in areas such as data security, human interpretation, and technology integration.
