AI, web, and standards Hidde de Vries, Web Standards for Policy Makers, 24 February 2026
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(I’m here in personal capacity)
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AI AI and web AI and web standards
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AI
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Students of AI often learn…
linguistics, computer science, mathemathics, psychology, neuroscience, philosophy of language, philosophy of science
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AI doesn’t mean the same to everyone, it’s an effective phrase to sell ideas
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Natural Language Process ng
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branch of AI concerned with read ng, wr t ng and commun cat ng in human languages (eg speech recognition, machine translation)
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Mach ne Learn ng
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the process in which machines learn to class fy by themselves after being shown a large amount of labelled examples
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Generat ve AI
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large language models that were trained to generate text, media or code in response to prompts
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“word calculators”
“near human intelligence”
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Agents
task-oriented systems that can act on a human’s behalf
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human toll data-taggers, people near data centres
copyr ght ignored and violated
major ethical concerns
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accidental or purposefully
susta nab l ty magnitudes more water, electricity used
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AI and web
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he web s open for everyone to read and wr te
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LLMs train on the open web
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LLMs train on the open web
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LLMs put a strain on the web
matthiasott.com/articles/webspace-invaders / drewdevault.com
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LLMs co-‘author’ the web
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LLMs co-‘author’ the web
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from: Alexandra Souly ea, Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples
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he web s where many access LLMs
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he web s where consequences of LLM usage are felt
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Impl cat ons of AI deployment for web users (the good)
Content may be more clear and relevant Easier to find things via more fuzzy search and structured result Very educated guesses at text alternatives
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More useful recommendations
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Impl cat ons of AI deployment for web users (the bad)
Possible increase of filter bubbles Decreased factuality of web content Security risks of AI agents acting on the web More phishing risk as it is less work
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Cost increase due to lower click through rates
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AI and web standards
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CEN/CENELEC
Web and AI IG
J C 21 + 5 working groups (many beyond web)
Web Machine Learning WG + CG
C Securing AI (SAI)
IETF
C2PA
AI Preferences WG Agent to Agent BOF
Content Credentials
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ETSI
CGs: AI Agent Protocol, Autonomous Agents on the Web, AI Knowledge Representation, Web AI for ime Series
ECMA
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W3C
C DA A
Agent c AI Foundat on MCP AGEN S.MD
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Web Machine Learning WG (W3C)
Web Neural Network API
github.com/webmachinelearning/webnn/blob/main/explainer.md
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Web Machine Learning WG (W3C)
Web Neural Network API dedicated low-level API for neural network inference hardware acceleration
github.com/webmachinelearning/webnn/blob/main/explainer.md
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Web Machine Learning WG (W3C)
Web Neural Network API object detection, face detection, machine translation, noise suppression
github.com/webmachinelearning/webnn/blob/main/explainer.md
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Google’s Chrome Built-in AI eam + Members of the Web Machine Learning CG (W3C)
Prompt API
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github.com/webmachinelearning/prompt-api
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Google’s Chrome Built-in AI eam + Members of the Web Machine Learning CG (W3C)
Prompt API
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github.com/webmachinelearning/prompt-api
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request to summarise
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request
summarised content
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request to summarise summarised content
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ranslat on API
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github.com/webmachinelearning/translation-api T
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Google’s Chrome Built-in AI eam + Members of the Web Machine Learning CG (W3C)
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AI Preferences WG (IE F)
A Vocabulary For Express ng AI Usage Preferences
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ietf-wg-aipref.github.io/drafts/draft-ietf-aipref-vocab.html
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D lemmas for spec authors
Could this feature inadvertently affect privacy? Could this feature result in misinformation? How can we balance privacy and accessibility? Is it possible for this feature to be deterministic?
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What sort of input may be missing? (re web standards on AI /web?
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uest on for you
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Questions and discussion hidde@hiddedevries.nl hidde.blog/slides