← Back to all news
arXiv Computation and Language By Organizers of QueerInAI, :, A Pranav, Alissa A. Valentine, Alex Markham, Beckett LeClair, Tereza Blazkova, Ekaterina Kornilitsina, Sofie H. Bruun, Gerasimos Spanakis, Anne Lauscher

Queering the Audits: Community-Based Auditing of AI Harms to Queer Communities

Read the original on arXiv Computation and Language →

The paper "Queering the Audits: Community-Based Auditing of AI Harms to Queer Communities" reports that AI systems often embed majority-group defaults, leading to harms such as erasure, misclassification, and discrimination against queer communities. Traditional technical audits, which rely on aggregate metrics, fail to detect these harms that become apparent only through lived experience. The authors conducted a participatory auditing workshop at EurIPS 2025, where 16 queer community members used the 4Cs harm taxonomy to audit four AI case studies, tracing harms to problem definition, data collection, and contractual structures that exploit vulnerable populations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv AI
Sep 12

Characterizing Bluesky Content Moderation Service: From Automation of Service to Landscape of Harms

The study audits Bluesky’s Moderation Service (BMS) using its 10.6 million public moderation labels from 2025. It finds that BMS operates as a human‑AI collaboration: sexual and graphic content is flagged automatically in seconds, while more nuanced or high‑stakes content requires human review that can take hours or days. The system shows high precision (0.837) but low recall (0.222), with annotators detecting 4.5 times more harmful content than the system, and clustering reveals harms ranging from hostility toward protected groups to the spread of explicit material.

By Pushpdeep Singh, Sayeh Jarollahi, Ayan Majumdar, Vabuk Pahari, Abhijnan Chakraborty, Krishna P. Gummadi, Ingmar Weber, Abhisek Dash
More like this →
arXiv AI
Oct 27

Safe by Benchmark, Unsafe in Practice: Lessons from 1331 Child-Related AI Incidents

The paper presents a child-centered harm taxonomy for AI systems and analyzes 1,331 publicly reported child-related AI incidents. It finds that harms are concentrated in representational, dignity-related, and psychological categories, often linked to deployment-context, governance, and misuse failures rather than model defects. The study highlights that model-level evaluations alone cannot anticipate the harm pathways most frequently documented for children, and recommends broader mitigations such as age assurance, consent verification, safety-by-design UX constraints, incident disclosure norms, and targeted auditing regimes.

By Junfeng Jiao, Kevin Chen, Shrestha Mishra, Abhejay Murali, David Atkinson, Shriti Raj, Saleh Afroogh
benchmarkssafety
More like this →
arXiv AI
Sep 24

An Open Pipeline and Dashboard for Systemic-Risk Evidence under the EU AI Act's Code of Practice

The paper introduces the Systemic Risk Index, an open pipeline and dashboard that aggregates evidence from 19 public AI benchmarks into four systemic‑risk categories defined by the EU GPAI Code of Practice. It evaluates 18 models using harm‑preserving perturbations and simulated deployment contexts, offering users the ability to switch between average and worst‑case aggregation and to trace each risk rating back to its benchmark evidence. The study finds that worst‑case scores can be 14 to 37 points lower than average scores, and that LLM judges agree with human graders at a level comparable to human‑human agreement.

By Jacob T. Emmerson, Phuong-Anh Nguyen-Le, Ronan Romano, Wilber Sean V. Anterola, Yann Billeter, Zhijing Jin
llmsroboticsbenchmarkssafety
More like this →