continual mechanization of human work, now accelerated by the integration of Artificial Intelligence (AI ) and Agentic AI. AI systems are being deployed to handle everything from customer service inquiries to complex data analysis We now have AI systems to make decisions and perform work with far-reaching consequences without the Approach AI and digital agents as tools to augment human wisdom, not replace it.

How should the use of AI be governed when used in safety devices or as part of a safety component? Here are some of the ways AI have improved DSS: Automated Data Analysis : AI algorithms can automatically Machine Learning : AI-enabled DSS can use machine learning algorithms to improve the accuracy of its Advantages of Autonomous Decision-Making using AI: Speed and efficiency: AI can analyze data at a much Disadvantages of Autonomous Decision Making using AI: Lack of human oversight: AI systems can make decisions

Alongside benefits of Digital Twins, the integration of Artificial Intelligence (AI) introduces additional AI algorithms enhance the analysis of data within digital twins, but they also introduce the risk of Moreover, AI-driven decisions may be opaque, making it challenging to understand their rationale and Additionally, complex AI models may lack interpretability, hindering decision-makers' ability to trust AI algorithms drive autonomous actions within digital twins, enhancing efficiency and responsiveness.

AI Exposes the Latency For many compliance programs, this misplaced ownership is a latent risk. In a growing number of domains, AI among them, an unowned obligation no longer waits to become a problem obligations — privacy, safety, quality, financial integrity, legal and ethical commitments — through AI An AI cannot own any of them. It cannot accept responsibility when an obligation is unmet. Deploying AI into an organization that already put ownership in the wrong place does not close the gap

When it comes to AI we must: Keep Humans In The Loop When there is a chance of harm, the decision to AI should not make ethical decisions for you. What steps can you take starting today to ensure your organization is responsible with its use of AI?

Today's AI is inference over data, used as a proxy for both knowledge and intelligence, with the layers An ontology of AI comes first, built from what these systems are and do. He writes about compliance, engineering, and AI at leancompliance.ca.

Understanding how systems behave—whether traditional software or advanced AI—has become essential not For AI systems, monitoring extends to model performance metrics, prediction latency, and data drift detection In AI contexts, observability encompasses the full model lifecycle—from data ingestion through training In AI systems—where complex models often operate as black boxes—explainability techniques like SHAP, Imperative As regulatory pressures intensify across industries—from GDPR's right to explanation to emerging AI

Nowhere is this clearer than in AI adoption. We are not governing AI. We are handing the work to AI agents that have no sense of where the organization is going — agents that

This incident has highlighted the limitations and risks associated with relying solely on AI-generated While ChatGPT and other similar AI tools provide utility across various industries, including the legal This incident not only raised questions about the accuracy of AI-generated content but also emphasized The use of AI tools should never substitute proper legal research and verification. legislation to regulate the use of AI where public safety may be at risk.

This is never more important now when it comes the use of Artificial Intelligence (AI). If organizations want to steer aware from harms associated from the use of AI in their value chain, they must explicitly state their objectives for the responsible use of AI.

processing (removal of waste), data lakes, machine learning, and other forms of artificial intelligence (AI We don’t think we our heads, we think with AI. And for that we need algorithms and AI where the rules are transparent and explainable for people to Don’t only think with your AI, think with your head.

The rise of AI has both amplified this challenge and brought it into sharp focus. As organizations face new obligations for transparency and explainability in their AI systems, they're deploying large language models for simple text tasks that simpler algorithms could handle, running complex AI The massive compute requirements of modern AI often exemplify this waste. 4. automated reports no one reads, robotic processes that move data unnecessarily between systems, or AI