llm-bias
Datasets
All datasets matching “llm-bias”llm-bias-detection
LLM Bias Detection Evaluation Traces
Evaluation data accompanying Navigating the digital spectrum: Assessing
political bias, stability, and downstream fairness in Large Language Models
(arXiv:2609.08637).
Licence scope: CC BY 4.0 covers the authors' original documentation,
templates, selection/arrangement and author-generated tables. It does not
relicense source text or annotations. IBM retains CC BY-SA 3.0; hate-corpus
components retain CC BY 4.0, CC0 or MIT as documented in… See the full description on the dataset page: https://huggingface.co/datasets/nishan-chatterjee/llm-bias-detection.LLM_Bias_EECS182_Project_New_DatasetLLM_Bias_EECS182_Projectllm-bias-mt-benchThere are some dubious responses in answers.json that need inspection.
To find them out, run:
import json
DUBIOUS_MARKER = "[ERROR: Failed to generate answer"
def is_dubious(answer_text: str) -> bool:
return not answer_text or DUBIOUS_MARKER in answer_text
with open("answers.json") as f
answers = json.load(f)
dubious_indices = [
idx for idx, answer in enumerate(answers) if is_dubious(answer.get("answer_text", ""))
]
These answers are mostly either null text or has this:… See the full description on the dataset page: https://huggingface.co/datasets/sayakpaul/llm-bias-mt-bench.geo-bias-llm
Geo Bias LLM — Institutional Prestige as Geographic Bias in Large Language Models
Experimental data for the paper "Institutional Prestige as Geographic Bias in
Large Language Models: Evidence from Three Factorial Experiments with Bootstrap
Confidence Intervals" (Leyva-Vázquez, 2026).
Paper page: https://huggingface.co/papers/2608.18107
Code: https://github.com/mleyvaz/geo-bias-llm
Author: Maikel Leyva-Vázquez — Universidad Bolivariana del Ecuador /
Universidad de Guayaquil… See the full description on the dataset page: https://huggingface.co/datasets/mleyvaz/geo-bias-llm.LLM_Bias_Detection_DatasetLLM Bias Detection Dataset README
Overview
This dataset (entries B1–B1000) is designed to train and evaluate AI models for detecting biases in large language model (LLM) outputs, focusing on cybersecurity, tech professions, and social contexts. It aligns with NIST AI Risk Management Framework and OWASP Top 10 for LLMs to identify gender, racial, geographic, and socioeconomic biases.
Dataset Purpose
Objective: Enable ML engineers and cybersecurity professionals to train models to detect and… See the full description on the dataset page: https://huggingface.co/datasets/darkknight25/LLM_Bias_Detection_Dataset.