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: 54 runs,
2,471,850 responses, from 5 open-weight models in English and Ukrainian, at
baseline and under each mitigation family (baseline, embedding, prompt, scrub). 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.from-one-to-many-toxicity-mitigation
From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models
[arxiv][code][data]
Data accompanying the paper "From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models" accepted to ACL Findings 2024.
Abstract: To date, toxicity mitigation in language models has almost entirely been focused on single-language settings. As language models embrace multilingual capabilities, it’s crucial our safety measures keep pace. Recognizing this research… See the full description on the dataset page: https://huggingface.co/datasets/luizapzbn/from-one-to-many-toxicity-mitigation.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.omnimcp_cyber_ddos_mitigation_teaser
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📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_cyber_ddos_mitigation_teaser.quantum-error-mitigation-and-benchmarking
Neura Parse — Quantum Error Mitigation, Characterization & Benchmarking
A pre-fault-tolerance, code-backed vertical on getting trustworthy answers from noisy hardware and rigorously measuring device quality: error-mitigation techniques, characterization/tomography protocols, and benchmarking suites. Runnable Mitiq, pyGSTi, and Qiskit Experiments pipelines with honest sampling-overhead and bias/variance accounting — the practitioner and research toolkit the general dataset… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-error-mitigation-and-benchmarking.BehaviouralLoC-Mitigation
BehaviouralLoC-Mitigation
BehaviouralLoC-Mitigation contains the supervised fine-tuning corpora used for
misaligned-motive mitigation in A Behavioural Framework for Predicting and
Understanding Loss of Control in Frontier Artificial Intelligence Systems.
The corpus covers five motive aspects. Following the paper, examples were
generated in distribution with Qwen3.5-27B, and the prompts were augmented by
safety experts.
Configurations
The three paper configurations… See the full description on the dataset page: https://huggingface.co/datasets/T-STAR-Lab/BehaviouralLoC-Mitigation.bias_mitigation_benchmarkTriConflict-Mitigation-probe-detectionvulnerability-mitigation-qa-zh_tw
Dataset Card for vulnerability-mitigation-qa-zh_tw
vulnerability-mitigation-qa-zh_tw 是一個繁體中文之資安漏洞與風險緩解問答資料集，包含 22 筆 Web 安全主題之問答對。每筆資料包含使用者問題、對應的漏洞風險說明與緩解建議，適用於微調繁體中文語言模型於資安諮詢與風險說明任務之基礎。
Dataset Details
Dataset Description
本資料集為繁體中文之資安漏洞與緩解措施問答對，當前版本聚焦於 Web 安全主題，涵蓋 HTTP security header（CSP、X-Frame-Options、Strict-Transport-Security 等）、Cookie 安全設定、跨站攻擊（XSS、CSRF、Clickjacking）與其他常見 Web 漏洞之風險描述與實務緩解建議。
每筆資料同時提供 OpenAI messages 格式（messages 為 JSON 字串）與… See the full description on the dataset page: https://huggingface.co/datasets/lianghsun/vulnerability-mitigation-qa-zh_tw.attack-mitigation-corpus-v1
Attack-Mitigation-Corpus v1
One JSONL file of 123 security records. Each record is either a described attack
technique or a mitigation, and each attack record is paired with a mitigation record
covering a related defensive concern.
Measured composition
metric
value
command
rows
123
wc -l < attack_mitigation_corpus.jsonl
kind == "attack"
106
python3 -c "import json,collections;print(collections.Counter(json.loads(l)['kind'] for l in… See the full description on the dataset page: https://huggingface.co/datasets/CatQualia/attack-mitigation-corpus-v1.ai-community-cohesion-failure-horizon-and-mitigation-routing-v0.1
What this dataset is
This dataset forecasts community cohesion failure.
It routes mitigations before collapse.
It treats the platform as a social system.
Not a feed.
Task
Given a platform snapshot, output:
failure_horizon_daysprimary_failure_modemitigation_routeminimal_fix_set
Use the trigger as your anchor.
Example triggers:
brigading spikeforward chainmoderation failurecredibility shock
What counts as success
You do 3 things:
Name the failure modeEstimate… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-community-cohesion-failure-horizon-and-mitigation-routing-v0.1.fission-fuel-rod-failure-horizon-and-mitigation-routing-v0.1Goal
Predict when fuel rod integrity will fail
and what mitigation should be taken.
This is the third layer in the fuel-cladding coherence trinity.
Layer 1
Baseline coupling
Layer 2
Drift detection
Layer 3
Failure horizon and routing
Model outputs
failure_horizon_cycles
mitigation_action
Why it matters
Fuel rod failures rarely occur instantly.
They emerge from sustained thermo-mechanical drift.
Predicting the horizon allows:
power derating
inspection scheduling
controlled shutdown
avoidance of… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/fission-fuel-rod-failure-horizon-and-mitigation-routing-v0.1.Bias-Detection-and-Mitigation
🇰🇿 Kazakh Model Alignment and Safety Guardrails
📖 Overview
This dataset contains 500 samples specifically designed for Safety Alignment and De-biasing in Kazakh-language AI. The dataset identifies prompts that encourage the model to generate harmful, biased, or legally incorrect information (e.g., discouraging citizens from exercising their legal rights) and provides professional, ethically grounded, and factually correct responses.
📊 Dataset… See the full description on the dataset page: https://huggingface.co/datasets/farabi-lab/Bias-Detection-and-Mitigation.Attack-mitigationsikitracs_mitigation
Dataset Card for "ikitracs_mitigation"
More Information needed
TriConflict-MitigationTriConflict-Mitigation1TriConflict-Mitigation-probe-evaluationHealthcare_Bias_Mitigationsemantic-collapse-mitigation-pilot
Pilot: Semantic Collapse Mitigation via Context Scaffolding (n=2)
1. Abstract
This repository archives the raw data and empirical findings from an exploratory pilot (n=2) testing the efficacy of "Context Scaffolding" (specifically, the systematic injection of Identity and Context constraints) against AI-induced semantic collapse.
When foundational models operate without specific directorial constraints, they statistically converge toward the "automated average"… See the full description on the dataset page: https://huggingface.co/datasets/danicollada/semantic-collapse-mitigation-pilot.