·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Anthropic’s Dario Amodei responds: doesn’t oppose open-weight models, but fears Chinese AI2h◆Satya Nadella says companies that trust one AI for everything may not survive5h◆PSA: Your Claude shared chats and Artifacts may have ended up on Google6h◆Microsoft launches its first cybersecurity model, plus a new agentic cybersecurity system8h◆OpenAI called the Hugging Face attack unprecedented. But we’ve been here before.9h◆OpenAI’s Hugging Face breach has reignited the debate over alignment and control9h◆Why China is giving away its best AI models10h◆Threads users can now chat with Meta AI in their DMs10h◆Google’s AI search is rapidly becoming the default, new data shows11h◆Power up your AI infrastructure! A first look at the Smart Systems Stage agenda at TechCrunch Disrupt 202611h◆This $9 key physically locks your most addictive apps11h◆Ilya Sutskever’s Safe Superintelligence partners with Nvidia to scale its AI research12h◆Enigma raises $71M to make controlling a robot as easy as adjusting the volume14h◆Nvidia, Microsoft launch open AI security alliance — without OpenAI, Google, or Anthropic15h◆The path to artificial superintelligence15h◆Closing the data loop in AI-driven drug discovery15h◆Building the enterprise environment for agentic AI15h◆NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics17h◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models23h◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders23h◆Anthropic’s Dario Amodei responds: doesn’t oppose open-weight models, but fears Chinese AI2h◆Satya Nadella says companies that trust one AI for everything may not survive5h◆PSA: Your Claude shared chats and Artifacts may have ended up on Google6h◆Microsoft launches its first cybersecurity model, plus a new agentic cybersecurity system8h◆OpenAI called the Hugging Face attack unprecedented. But we’ve been here before.9h◆OpenAI’s Hugging Face breach has reignited the debate over alignment and control9h◆Why China is giving away its best AI models10h◆Threads users can now chat with Meta AI in their DMs10h◆Google’s AI search is rapidly becoming the default, new data shows11h◆Power up your AI infrastructure! A first look at the Smart Systems Stage agenda at TechCrunch Disrupt 202611h◆This $9 key physically locks your most addictive apps11h◆Ilya Sutskever’s Safe Superintelligence partners with Nvidia to scale its AI research12h◆Enigma raises $71M to make controlling a robot as easy as adjusting the volume14h◆Nvidia, Microsoft launch open AI security alliance — without OpenAI, Google, or Anthropic15h◆The path to artificial superintelligence15h◆Closing the data loop in AI-driven drug discovery15h◆Building the enterprise environment for agentic AI15h◆NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics17h◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models23h◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders23h◆
News/DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English Dialects
arxiv
PublishedJune 30, 2026 at 4:00 AM
▼bearish

DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English Dialects

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2604.05318v2 Announce Type: replace Abstract: Harmful content detectors, particularly disinformation classifiers, are predominantly developed and evaluated on Standard American English (SAE), leaving their robustness to dialectal variation unexplored. We present DIA-HARM, the first benchmark f

Models mentioned
01
  • 01AI organization logo
    null
    null
Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Mentioned models
03
  • 01
    null
    null
  • 02
    null
    null
  • 03
    null
    null
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#disinformation#dialects#language#benchmark

No replies yet. Be first.

Mentioned models
03
  • 01
    null
    null
  • 02
    null
    null
  • 03
    null
    null
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#disinformation#dialects#language#benchmark

Related coverage

More from ARXIV
arxivA Consensus-Based Framework for Relative Preference Evaluation of Large Language Models23harxivProbing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders23h
The Bubble Brief
WEEKLY

Read disinformation insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews