Artificial intelligence is at a critical juncture, with many enterprise AI initiatives failing to reach production and exposing organizations to significant risks. Effective AI governance is essential to prevent these issues, ensuring that AI systems are treated as vital assets requiring ongoing oversight and adaptation.
The federal government aims to enhance productivity by implementing artificial intelligence (AI) across various sectors, but emphasizes the importance of thoughtful deployment to avoid wasting public funds. It warns that without adequate oversight and expertise, AI tools could lead to significant risks related to privacy, ethics, and environmental impact.
On July 1, 2025, the U.S. Senate voted to reject a proposed ten-year moratorium on state-level AI regulation, allowing individual states to legislate independently. This decision creates a fragmented compliance environment for insurance carriers that must navigate varying state laws regarding AI use.
On July 1, 2025, the U.S. Senate voted to reject a proposed ten-year moratorium on state-level AI regulation, allowing individual states to legislate independently. This decision creates a fragmented compliance environment for insurance carriers that must navigate varying state laws regarding AI use.
Lobbyists are making a final effort to delay the rules for General Purpose AI (GPAI) as the European Commission prepares to publish the voluntary Code of Practice. Despite these attempts, the Commission has indicated that the GPAI rules will still apply beginning in August.
Human-in-the-Loop (HITL) is a collaborative approach that integrates human expertise into the lifecycle of AI systems, ensuring optimal results by leveraging both human judgment and machine efficiency. This method is particularly effective in handling ambiguous situations and ethical considerations, making it essential for the responsible development of AI technologies.
AI cooperatives, which operate on cooperative principles, present a promising alternative to the current model dominated by a few large firms. By promoting democratic governance and shared ownership, these cooperatives can address issues like privacy violations and biases, creating a more accountable and community-centered approach to AI development.
This research project investigates how the EU Artificial Intelligence Act is transforming corporate governance and accountability frameworks, compelling companies to reconfigure responsibilities and anticipate liability throughout their structure. It highlights the need for a model of distributed accountability that reflects the increasing integration of operational decision-making with legal compliance.
This research project investigates how the EU Artificial Intelligence Act is transforming corporate governance and accountability frameworks, compelling companies to reconfigure responsibilities and anticipate liability throughout their structure. It highlights the need for a model of distributed accountability that reflects the increasing integration of operational decision-making with legal compliance.
Artificial intelligence is becoming essential for the risk function, helping chief risk officers (CROs) to navigate compliance and data governance challenges. With a growing number of organizations adopting AI technologies, it is crucial for CROs to develop effective AI strategies to manage risks and drive innovation.