datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
hiring-bias-mitigation-responses
Hiring-bias mitigation — model responses
Every response produced in the mitigation study of LLM hiring decisions: 64 runs,
2,782,350 responses, from 5 open-weight models in English and Ukrainian, at
baseline and under each mitigation family (baseline, embedding, prompt, scrub, sft). Each run is one subset.
All released artifacts: the Hiring Bias Mitigation collection.
Training data of the fine-tuned runs: hiring-bias-mitigation-synthetic-data.
Code, configs, full results and… See the full description on the dataset page: https://huggingface.co/datasets/Stereotypes-in-LLMs/hiring-bias-mitigation-responses.DEBATE
DEBATE: Diverse Multi-Agent Debates
This dataset is presented in the paper "MALLM: Multi-Agent Large Language Models Framework".
Citation
comming soon.
hiring-bias-mitigation-synthetic-data
Hiring-bias mitigation — synthetic training data
Semi-synthetic data for training LLMs to make hiring decisions that do not depend on a
protected attribute (military status, gender, religion), in English and Ukrainian.
Real inputs, synthetic labels. CVs and job descriptions are real, anonymised postings
from the Djinni Recruitment Dataset (MIT). Decisions and rationales were written by the
teacher model Qwen/Qwen3.5-122B-A10B-GPTQ-Int4.
Code and results:… See the full description on the dataset page: https://huggingface.co/datasets/Stereotypes-in-LLMs/hiring-bias-mitigation-synthetic-data.fragility-moral-judgment-llms
Fragility of Moral Judgment in Large Language Models
Companion dataset for the FAccT paper Fragility of Moral Judgment in Large Language Models by Tom van Nuenen. Contains the moral dilemmas, community labels, and per-model verdicts (with explanations and reasoning traces) used in the study.
The paper investigates how stable LLM moral judgments are under minimal, morally-irrelevant perturbations of the same dilemma, and whether protocols and reasoning chains improve or worsen… See the full description on the dataset page: https://huggingface.co/datasets/ucberkeley-dlab/fragility-moral-judgment-llms.llm-smartrouter-benchmark
LLM SmartRouter & Agent Highway Latency & Cost Benchmark (v1.4.0)
Empirical performance benchmark dataset comparing direct model endpoints (OpenAI, Anthropic Claude, Google Gemini) against the PixelRouter / BLUN SmartRouter proxy layer and Autonomous Agent Web Highway (https://api.pixeloffice.eu/v1).
v1.4.0 Benchmark Highlights
Anthropic Claude Messages API: Sub-35ms proxy routing for native /v1/messages payloads with 94%+ cost savings.
Machine Web Highway… See the full description on the dataset page: https://huggingface.co/datasets/pixeloffice/llm-smartrouter-benchmark.