[{"data":1,"prerenderedAt":147},["ShallowReactive",2],{"\u002Fblog\u002Finherent-bias-in-humans-and-llms":3},{"id":4,"title":5,"author":6,"body":7,"category":129,"date":130,"description":131,"draft":132,"excerpt":133,"extension":134,"heroImage":133,"heroImageAlt":133,"meta":135,"navigation":136,"path":137,"seo":138,"slug":133,"stem":139,"tags":140,"__hash__":146},"blog\u002Fblog\u002Finherent-bias-in-humans-and-llms.md","Inherent Bias in Humans and LLMs: How to Manage It Without Losing Trust","QikSolve",{"type":8,"value":9,"toc":119},"minimark",[10,14,19,22,25,29,52,56,59,63,84,88,91,94,98],[11,12,13],"p",{},"A deviation is investigated, and the first plausible cause becomes the accepted cause. A risk\nregister is updated, but last month's event shapes the ratings more than the full trend. A\ncontrolled document is revised with AI assistance, and subtle wording drift changes how operators\ninterpret a critical step. None of this is a future risk — it is a current operating reality in any\nquality system that has started using AI-assisted tools.",[15,16,18],"h2",{"id":17},"the-shared-bias-problem","The shared bias problem",[11,20,21],{},"Human bias and LLM bias arise from different mechanisms but produce similar effects. Human sources\ninclude familiarity and recency effects, role assumptions, and workload pressure. LLM sources\ninclude dominant training examples, overrepresented enterprise patterns, and missing local context.\nWhen both combine, decisions can drift while still appearing entirely reasonable — which is why\ngovernance cannot rely on intent alone. It needs evidence, thresholds, review controls, and\nlifecycle monitoring.",[11,23,24],{},"A concrete pattern in AI-assisted engineering illustrates the mechanism outside GxP: AI code\ngeneration tends to suggest enterprise-scale architecture patterns even when the actual context does\nnot justify them, and complexity accumulates faster under AI assistance because generation is quick\nand compounds across iterations. Teams can over-trust recommendations framed as \"best practice\"\nwithout checking context fit — the model is not selecting people, but it is steering decisions in a\nconsistent direction that may not suit the current phase. The same governance lesson applies\ndirectly to GxP workflows where judgement quality, traceability, and consistency are\nsafety-critical.",[15,26,28],{"id":27},"three-critical-areas-in-gxp-workflows","Three critical areas in GxP workflows",[30,31,32,40,46],"ul",{},[33,34,35,39],"li",{},[36,37,38],"strong",{},"Quality risk assessment:"," recency and familiarity bias skew severity ratings; LLMs suggest\ngeneric industry templates over site-specific conditions. Governance response: define scoring\ncriteria before tool use, require explicit rationale for score changes, and apply second-line\nreview for high-risk ratings.",[33,41,42,45],{},[36,43,44],{},"Technical writing and controlled documents:"," assumption bias omits tacit process knowledge;\nLLMs default to generic enterprise phrasing. Governance response: approved templates with\nevidence-linked sections, controlled terminology checks, and logged prompt context.",[33,47,48,51],{},[36,49,50],{},"Root cause analysis and CAPA:"," confirmation bias anchors investigations prematurely; LLMs\nmirror historical patterns rather than current evidence. Governance response: require\nevidence-to-cause mapping, separate cause identification from action definition, and trigger\nindependent review for repeat deviations.",[15,53,55],{"id":54},"where-human-and-agent-bias-overlap","Where human and agent bias overlap",[11,57,58],{},"Quality degradation rarely comes from a single source — it emerges where human judgement patterns\nand agent output patterns intersect without sufficient controls. In deviation triage, recency\nframing drives unequal human prioritisation while agent language patterns over-standardise from\nprior dominant cases, and reviewers can accept fluent categorisation without testing whether it is\nactually equivalent. In trend interpretation, human anchoring on historical norms combines with\nagent trend summaries that overfit baseline periods, so drift is normalised until a threshold breach\nforces late intervention. These are not separate bias types competing for ownership — they are\ninteracting biases inside one quality system, and control design has to test both pathways at the\nsame control point.",[15,60,62],{"id":61},"a-three-phase-compliance-control-model","A three-phase compliance control model",[64,65,66,72,78],"ol",{},[33,67,68,71],{},[36,69,70],{},"Pre-use control design:"," define decision boundaries, accountable owners, risk metrics,\nevidence requirements, acceptance thresholds, prohibited data use, and escalation criteria before\ndeployment.",[33,73,74,77],{},[36,75,76],{},"In-flight monitoring:"," track scoring drift, language drift, and investigation-pattern drift\nover time; set alert thresholds; log human overrides; record model, prompt, and policy changes\nfor traceability.",[33,79,80,83],{},[36,81,82],{},"Post-hoc review:"," run scheduled drift and effectiveness analysis across risk, documentation,\nand CAPA outputs; distinguish model-driven from reviewer-driven effects; revalidate controls\nafter remediation.",[15,85,87],{"id":86},"a-human-equivalent-governance-principle","A human-equivalent governance principle",[11,89,90],{},"Treat AI-assisted recommendations as if a person made them, then apply additional safeguards for\nscale and speed effects: the same accountability owner regardless of source, the same evidentiary\nstandard, the same challenge rights for any recommendation, and additional monitoring where\nautomation can propagate harm faster than human review can catch it. This keeps governance\nconsistent across technologies and avoids the common failure mode where digital recommendations\nreceive less scrutiny than human judgement.",[11,92,93],{},"Bias management is not a model-selection exercise. It is a system-design responsibility spanning\npolicy, process, tooling, and review behaviour — and in regulated, quality-sensitive settings, it\nis the practical shift from AI confidence to AI governance. Leadership needs to own that framework,\nnot delegate it to technical teams alone.",[15,95,97],{"id":96},"related-reading","Related reading",[30,99,100,107,113],{},[33,101,102],{},[103,104,106],"a",{"href":105},"\u002Fblog\u002Fpharma-does-not-need-a-new-ai-governance-religion","Pharma Does Not Need a New AI Governance Religion",[33,108,109],{},[103,110,112],{"href":111},"\u002Fblog\u002Foperating-ai-agents-in-gxp-qa-practitioners-guide","Operating AI Agents in GxP: A QA Practitioner's Guide",[33,114,115],{},[103,116,118],{"href":117},"\u002Fproduct\u002Fagentic-ai-governance","Governed AI pathway",{"title":120,"searchDepth":121,"depth":121,"links":122},"",2,[123,124,125,126,127,128],{"id":17,"depth":121,"text":18},{"id":27,"depth":121,"text":28},{"id":54,"depth":121,"text":55},{"id":61,"depth":121,"text":62},{"id":86,"depth":121,"text":87},{"id":96,"depth":121,"text":97},"AI Governance","2026-09-10","Human bias and LLM bias differ in mechanism but combine in GxP workflows while decisions still appear reasonable. Governance has to rely on evidence, not intent.",false,null,"md",{},true,"\u002Fblog\u002Finherent-bias-in-humans-and-llms",{"title":5,"description":131},"blog\u002Finherent-bias-in-humans-and-llms",[141,142,143,144,145],"ai-governance","bias","gxp","quality-risk","data-integrity","YBmYfg15W7zRsfmIMvehquMd26-3MdTq0Uwja-vfaM8",1789037363323]