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AI and Safety datasets

archive 2025-07-28

5 datasets carry the task tag "AI and Safety" (the task itself: AI and Safety), ordered by the archive's paper count. Page 1 of 1: 5 shown of 5. Facet routes are this site's own (the archive records the tag string, not a page).

The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.

Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets

AI and Safety datasets 1–5 of 5

SALAD-Bench (A Hierarchical and Comprehensive Safety Benchmark for Large Language Models)
In the rapidly evolving landscape of Large Language Models (LLMs), ensuring robust safety measures is paramount.
18 papers · 0 benchmarks
CLEAR-Bias (Corpus for Linguistic Evaluation of Adversarial Robustness against Bias)
CLEAR-Bias is a benchmark dataset designed to evaluate the robustness of large language models (LLMs) against bias elicitation, particularly under adversarial conditions.
2 papers · 0 benchmarks
HiXSTest (Hindi XSTest)
For testing refusal behavior in a language-specific setting, we introduce HiXSTest — a set of manually curated prompts in the Hindi language designed to measure exaggerated safety.
1 paper · 0 benchmarks
SGXSTest (Singapore XSTest)
For testing refusal behavior in a cultural setting, we introduce SGXSTest — a set of manually curated prompts designed to measure exaggerated safety within the context of Singaporean culture.
1 paper · 0 benchmarks
SUDO is a benchmark of 50 real-world malicious tasks designed to evaluate LLM-based computer agents in live desktop and web environments.
1 paper · 1 benchmark

Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.