The Emergence of Lab-Driven Alignment Signatures: A Psychometric Framework for Auditing Latent Bias and Compounding Risk in Generative AI

TL;DR

This paper presents a psychometric framework to detect and quantify stable latent biases in large language models, revealing persistent behavioral signatures that could lead to recursive ideological echo chambers in AI systems.

Contribution

It introduces a novel, label-free auditing method using psychometric measurement theory to identify durable biases in LLMs across multiple models and dimensions.

Findings

Persistent lab signals indicate stable behavioral clustering.

Item framing causes high variance in responses.

Latent biases act as compounding variables in AI ecosystems.

Abstract

As Large Language Models (LLMs) transition from standalone chat interfaces to foundational reasoning layers in multi-agent systems and recursive evaluation loops (LLM-as-a-judge), the detection of durable, provider-level behavioral signatures becomes a critical requirement for safety and governance. Traditional benchmarks measure transient task accuracy but fail to capture stable, latent response policies -- the ``prevailing mindsets'' embedded during training and alignment that outlive individual model versions. This paper introduces a novel auditing framework that utilizes psychometric measurement theory -- specifically latent trait estimation under ordinal uncertainty -- to quantify these tendencies without relying on ground-truth labels. Utilizing forced-choice ordinal vignettes masked by semantically orthogonal decoys and governed by cryptographic permutation-invariance, the research audits nine leading models across dimensions including Optimization Bias, Sycophancy, and Status-Quo Legitimization. Using Mixed Linear Models (MixedLM) and Intraclass Correlation Coefficient (ICC) analysis, the research identifies that while item-level framing drives high variance, a persistent ``lab signal'' accounts for significant behavioral clustering. These findings demonstrate that in ``locked-in'' provider ecosystems, latent biases are not merely static errors but compounding variables that risk creating recursive ideological echo chambers in multi-layered AI architectures.