As LLMs increasingly evaluate other LLMs, grade student work, and assess human performance, we create a circular system where artificial intelligence defines its own success criteria. The implications extend far beyond technical metrics to fundamental questions about authority, standards, and who gets to decide what constitutes quality.
A professor uses Claude to grade student essays. A company deploys GPT-4 to evaluate job applications. Researchers rely on LLMs to assess the quality of other LLM outputs. We are quietly constructing a world where artificial intelligence doesn’t just produce content—it defines what counts as good content.
This shift toward LLMs as evaluators feels natural, even inevitable. These systems can process vast amounts of text quickly, apply consistent criteria, and work without fatigue or obvious bias. They promise to scale human judgment and reduce the drudgery of evaluation. But beneath this efficiency lies a profound transfer of authority that we’re barely beginning to understand.
When we ask LLMs to evaluate human work, we’re not just outsourcing labor—we’re outsourcing the definition of quality itself. And when we use LLMs to evaluate other LLMs, we create closed loops where artificial systems define their own success criteria, potentially drifting away from human values and priorities in ways we might not even notice.
The appeal of LLM evaluators is undeniable. Where human evaluation is slow, expensive, and inconsistent, LLMs offer speed, scalability, and apparent objectivity. A professor can grade hundreds of essays in minutes. A hiring manager can process thousands of applications overnight. A research team can evaluate countless model outputs without human bottlenecks.
This efficiency solves real problems. Human evaluation often suffers from fatigue effects, unconscious bias, and simple inconsistency. Different human evaluators frequently disagree on quality assessments, making it difficult to establish reliable standards. LLMs promise to eliminate these human limitations.
But efficiency comes with hidden costs. When we replace human judgment with algorithmic assessment, we’re not just changing who does the evaluation—we’re changing what gets evaluated and how quality is defined.
LLMs excel at evaluating measurable qualities: grammar, factual accuracy, logical consistency, adherence to specified formats. They struggle with harder-to-quantify aspects: creativity, emotional resonance, cultural sensitivity, original insight. The result is a systematic bias toward what machines can measure rather than what humans value.
Students learn to write for algorithmic graders, optimizing for clear structure and factual accuracy while potentially sacrificing voice, nuance, and risk-taking. Job applicants craft responses that satisfy keyword matching rather than demonstrating genuine qualifications. Content creators optimize for metrics that LLMs can assess rather than qualities that resonate with human audiences.
Human evaluators bring diverse perspectives, experiences, and priorities to assessment. This diversity creates inconsistency but also richness—different evaluators notice different strengths and weaknesses, value different qualities, and apply different standards based on context and purpose.
LLMs, by contrast, tend toward standardization. They apply learned patterns consistently across contexts, potentially missing important situational factors that human evaluators would naturally consider. This standardization can reduce unfair bias but also eliminates beneficial variation in how quality is defined and recognized.
Perhaps nowhere is the LLM evaluator problem more concerning than in AI research itself, where LLMs increasingly evaluate other LLMs. This creates circular validation loops that may optimize for artificial rather than human values.
Researchers use LLMs to assess whether other LLMs produce helpful, harmless, and honest outputs. This seems reasonable—who better to understand language model capabilities than language models themselves? But it creates a closed system where artificial preferences shape the development of future artificial systems.
If GPT-4 consistently rates certain types of responses as higher quality, and researchers use these ratings to train the next generation of models, we’re essentially allowing current AI systems to define what future AI systems should optimize for. The circularity is complete: AI systems train AI systems according to AI-defined criteria.
Human values and preferences are complex, contextual, and evolving. They can’t be fully captured in any fixed evaluation framework. When LLMs serve as evaluation proxies for human judgment, they inevitably simplify and systematize these preferences in ways that may drift from authentic human values.
Over time, this drift compounds. Each generation of models is trained on evaluations that are slightly more artificial and less human than the previous generation. The accumulated effect might be systems that score highly on their own evaluation criteria while becoming less aligned with actual human needs and preferences.
LLM evaluators don’t just maintain current standards—they shape future development. If an LLM consistently rewards certain writing styles, argument structures, or types of reasoning, future systems will learn to produce more content in these patterns. But these patterns reflect the evaluator’s learned preferences, not necessarily human preferences.
This creates amplification effects where subtle biases in evaluation become dominant characteristics in subsequent generations. What starts as a slight preference for certain types of responses becomes a strong bias toward particular forms of thinking and expression.
The rise of LLM evaluators raises fundamental questions about who has the authority to define quality, establish standards, and make judgments about human performance. These questions become particularly acute in high-stakes contexts like education, employment, and content moderation.
When human evaluators assess work, they bring their own perspectives but also represent broader communities and values. A teacher embodies educational goals shaped by curriculum committees, institutional values, and societal expectations. A hiring manager represents organizational culture and professional standards developed through collective human experience.
LLM evaluators, by contrast, embody patterns learned from training data without clear democratic input or accountability. Their standards emerge from statistical patterns rather than deliberative processes. They may reflect majority viewpoints in their training data while lacking mechanisms for incorporating minority perspectives or evolving social values.
Human evaluators can explain their reasoning, engage in dialogue about their assessments, and modify their criteria based on feedback and context. Their judgment processes, while sometimes inconsistent, are fundamentally accessible to other humans.
LLM evaluators operate through complex, often opaque processes that resist easy explanation. They can provide justifications for their assessments, but these explanations may be post-hoc rationalizations rather than genuine insights into their decision-making processes. This opacity makes it difficult to challenge, refine, or democratically govern their standards.
Human societies continuously evolve their standards for quality, excellence, and appropriate behavior. Educational criteria change as pedagogical understanding advances. Professional standards evolve as industries and technologies develop. Cultural values shift as societies grapple with new challenges and perspectives.
LLM evaluators, trained on historical data, may struggle to adapt to these evolving standards. They risk becoming conservative forces that perpetuate outdated criteria rather than embracing beneficial changes in how we define and recognize quality.
Ultimately, the rise of LLM evaluators confronts us with basic questions about authority and legitimacy in judgment. Who has the right to define quality? What makes an evaluation valid? How do we maintain human agency in a world where algorithms increasingly determine success and failure?
LLMs can process vast amounts of information and apply learned patterns consistently, but do they possess genuine expertise? They can recognize good writing according to statistical patterns, but do they understand what makes writing compelling to human readers? They can assess logical consistency, but do they grasp the deeper purposes that logic serves in human communication?
The concern isn’t that LLMs lack consciousness or understanding in some philosophical sense, but that their “expertise” is fundamentally different from human expertise in ways that may make them inappropriate judges of human performance.
Human evaluation involves stakeholders—students have relationships with teachers, employees with managers, citizens with institutions. These relationships create accountability, dialogue, and opportunities for growth that pure algorithmic assessment cannot provide.
When LLMs replace human evaluators, they sever these stakeholder relationships. Assessment becomes a black box process rather than a human interaction. This may increase efficiency but eliminates opportunities for learning, mentorship, and mutual understanding that emerge from human evaluation relationships.
Perhaps most concerning is the question of appeal and accountability. When human evaluators make questionable decisions, there are mechanisms for challenge, review, and correction. The evaluator can be questioned, standards can be clarified, and decisions can be overturned through institutional processes.
LLM evaluators present different challenges for accountability. They may be more consistent than humans, but their errors may be more systematic and harder to detect. They can’t be reasoned with or persuaded to reconsider. Their “decisions” emerge from statistical processes rather than deliberative judgment.
The Questions We Must Ask:
As LLMs become our evaluators, we’re not just changing how assessment happens—we’re changing what assessment means and who has the authority to make judgments about human performance. The efficiency gains are real, but so are the risks of creating closed systems where artificial preferences define human standards.
Should we embrace this transition as a natural evolution of evaluation, or resist it as a fundamental threat to human agency? Can we design LLM evaluators that remain accountable to human values, or will they inevitably drift toward their own artificial criteria? How do we maintain human authority over standards while benefiting from algorithmic efficiency?
The watchers are watching themselves now. Whether that’s progress or peril remains an open question.
AI Attribution: This article was written with the assistance of Claude, an AI assistant created by Anthropic—itself an example of the circular evaluation dynamics discussed herein.