AI and Systems

Why errors compound across AI agent steps, what the MAST research and OWASP ASI08 say, and design patterns that stop a cascade.

Automation can quietly erode the human skills a system relies on. Deskilling and the automation paradox explain the hidden cost.

A model can decay without changing a line of code, because the world moved. Concept drift shows why AI needs ongoing maintenance.

Recommendation systems and users co-create filter bubbles through feedback. Here is why the loop narrows your world and how to break it.

AI pursues the goal you set, not the one you meant. Reward hacking shows why the gap between measure and intent matters.

AI Governance helps analysts move beyond isolated events toward patterns, relationships, delayed effects, and the structures shaping behavior. This guid

AI governance explained as a feedback system: decision rights, sensing, adaptation, failure modes, and how NIST, ISO 42001 and the EU AI Act fit.

Human-AI Feedback Loop helps analysts move beyond isolated events toward patterns, relationships, delayed effects, and the structures shaping behavior.

Human-AI Feedback Loop helps analysts move beyond isolated events toward patterns, relationships, delayed effects, and the structures shaping behavior.

Algorithmic Monoculture helps analysts move beyond isolated events toward patterns, relationships, delayed effects, and the structures shaping behavior.