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Evidence record 1534 · automatically gathered

DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs

arXiv:2607.11228v1 Announce Type: new Abstract: While Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities, they remain highly susceptible to embedded social biases. Existing bias evaluation protocols predominantly rely on static datasets, which provide only a superficial assessment, as their fixed test cases cannot adaptively evolve to measure the true depth and limits of model vulnerabilities. We introduce DeepBias, an adaptive framework for the in-depth probing of social b

Record details

Published: 14 July 2026
Source: arXiv cs.CY
Category: Research
Topics: Bias & fairness
Retrieved: 14 July 2026

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Shared topic: Bias & fairness

Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction

arXiv cs.CY · 14 July 2026

Shared topic: Bias & fairness

The Benchmark Ceiling: Human Judgment, Evaluation Scarcity, and the Political Economy of AI Capability Measurement

arXiv cs.CY · 14 July 2026

Shared topic: Bias & fairness

How Data Narratives Go Wrong: A Taxonomy of Issues Across the Data Communication Process

arXiv cs.CY · 14 July 2026

Shared topic: Bias & fairness

Neutralizing Structural Inequality in the Nigerian FinTech Sector

arXiv cs.CY · 14 July 2026

Shared topic: Bias & fairness

Programming Language Policy as an AI Literacy Equity Problem: A 15-Nation Comparative Analysis

arXiv cs.CY · 14 July 2026

Shared topic: Bias & fairness

Can Induced Emotion Bias LLM Behaviors in Sequential Decision Making?

arXiv cs.AI · 14 July 2026

How to cite this record

ethics.ai (14 July 2026), “DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs,” evidence record 1534, https://ethics.ai/record/1534 (originally published by arXiv cs.CY).

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