AI for Human Factors and Safety: A Technical Primer¶
This wiki provides a comprehensive technical reference on AI systems — large language models, agents, and multi-agent architectures — written for Human Factors and Safety specialists working in complex work domains. It bridges the gap between AI technical literature and the needs of professionals responsible for designing, evaluating, and regulating work systems where AI plays an advisory role.
Foundations¶
- llm-architecture — Transformers, self-attention, tokenisation, statelessness
- context-windows — Working memory limits, lost-in-the-middle, context rot
- training-and-alignment — Pre-training, RLHF, Constitutional AI, fine-tuning
- inference-and-generation — Sampling, temperature, reasoning models, structured output
Agent Architectures¶
- perceive-reason-act-loop — The agent loop, ReAct pattern, autonomy scale
- tool-calling — How agents invoke external tools, failure modes, compounding
- memory-architectures — Episodic, semantic, procedural memory; collaborative design
- model-context-protocol — MCP standard for model-agnostic tool connectivity
Multi-Agent Patterns¶
- multi-agent-taxonomy — Taxonomies of multi-agent system types and configurations
- multi-agent-roles — Roles and responsibilities within multi-agent architectures
Failure Modes¶
- hallucination — Fluent, confident, and wrong
- calibration-and-confidence — Confidence does not match accuracy
- sycophancy — Agrees with user rather than providing independent analysis
- prompt-sensitivity — Minor input changes, major output variation
- output-vacuity — Superficially competent but operationally empty (slop)
- self-correction-limitations — Intrinsic correction fails; extrinsic correction works
- context-management-risks — Summarisation hallucination, information loss
- multi-agent-coordination-failures — Coordination breakdowns and cascading errors in multi-agent systems
Safety and Reliability¶
- non-determinism-and-reproducibility — Stochastic output and V&V challenges
- degradation-characteristics — Gradual, catastrophic, and silent degradation
- opacity-and-explainability — Double opacity and auditability barriers
- deployment-local-vs-cloud — Local vs cloud, open-weight models, configuration control
- epistemic-independence — Independence of reasoning and evidence in multi-agent systems
- monoculture-collapse — Common-cause failure from shared model weights and training data
Design Patterns¶
- retrieval-augmented-generation — RAG architecture and failure modes
- knowledge-graphs — Structured grounding and guardrails
- hybrid-decision-pipeline — Decision-type spectrum and layered architecture
- governance-gates — Human-in-the-loop checkpoints and approval gates in AI pipelines
- delivery-modes — Modes of AI output delivery and their implications for human oversight
Human-AI Interaction¶
- situation-awareness-in-human-ai-teams — Situation awareness theory applied to human-AI teaming contexts
- automation-bias — Over-reliance on automated systems and degraded independent judgment
- trust-calibration — Matching human trust levels to actual AI system reliability
- skill-degradation — Erosion of human competencies through over-reliance on AI assistance
- theory-of-mind-in-llms — Whether LLMs model others' mental states, and implications for trust and manipulation
Evaluation and Testing¶
- hra-methods-for-ai — Adapting human reliability analysis methods to AI-inclusive sociotechnical systems
- ai-specific-performance-shaping-factors — Factors that shape human performance when working with AI systems
- capability-gradient — Assessing AI capability across task types, complexity levels, and operational conditions
Domain Applications¶
- nuclear-ai-regulatory-considerations — Trilateral regulatory principles for AI in nuclear applications