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Evidence record 13076 · automatically gathered

Language Models Embody and Amplify Human Cognitive Distortions: What Is to Be Done?

Human judgment is fundamentally prone to error. A promise of AI is that it will rid decisions of bias and ensure a fairer and safer world for all. Yet research unequivocally demonstrates that LLMs exhibit consequential sociocognitive biases. We alert readers that bias in AI (a) is covert and ironically a feature of alignment goals, (b) is not merely a mirror, but an amplifier of human bias, (c) intensifies across model generations, and (d) even transmits bias to humans. Given the potentially sei

Record details

Published: 22 July 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 25 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Safety & alignment

PG-KINN: A Physics-Informed Petrov-Galerkin Kolmogorov-Arnold Network for Solving Forward and Inverse PDEs

arXiv cs.LG · 22 July 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

Sympathetic Framing: Evaluating AI Alignment across Sociodemographic Groups

arXiv cs.CL (ethics-relevant NLP) · 22 July 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

Spectral Prior for Reducing Exposure Bias in Diffusion Models

HuggingFace Daily Papers · 23 July 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

How Does Alignment Tuning Shape Representations of Sycophancy and Related Cue-Induced Biases in LLMs?

arXiv · 20 July 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

Directional Influence Function: Estimating Training Data Influence in Constrained Learning

arXiv fairness query · 25 July 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

Directional Influence Function: Estimating Training Data Influence in Constrained Learning

arXiv fairness query · 25 July 2026

How to cite this record

ethics.ai (22 July 2026), “Language Models Embody and Amplify Human Cognitive Distortions: What Is to Be Done?,” evidence record 13076, https://ethics.ai/record/13076 (originally published by arXiv).

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