Research and EU regulation for fashion AI, digested
Latest articles from Fashion AI Lab EU.
Benchmark figures in fashion AI papers and vendor decks rarely survive close scrutiny. This tutorial gives research, product, and procurement leads a repeatable method for stress-testing any accuracy claim before it enters a decision or a citation.
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The EU Data Act creates access and sharing obligations that touch every connected product in a supply chain, including textiles. This explainer maps those obligations to GS1 EPCIS and the horizontal data-space architecture, and states plainly where a textile-specific framework does not yet exist.
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Harmonised standards under the EU AI Act offer fashion AI teams a structured path to presumption of conformity—but most of those standards do not yet exist. Here is what that gap means for your compliance programme today.
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The revised Waste Framework Directive mandates textile EPR across all EU member states, but national implementation varies significantly. Here is what compliance and operations leads need to know.
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Two converging EU regimes — corporate sustainability due diligence and the forced labour product ban — require apparel brands to hold structured, auditable data about every tier of their supply chain. This article maps the specific data obligations and identifies where PLM and sourcing systems…
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The EU's ban on generic environmental claims is now law. This explainer sets out exactly what substantiation and verification a fashion brand must have in place before any green claim reaches a consumer.
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The Corporate Sustainability Reporting Directive imposes a structured set of ESRS datapoints on large apparel and footwear groups—but which are mandatory, which depend on materiality, and how far does the reporting boundary extend into outsourced supply chains? This article sets out the answers.
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As EU skills research confirms accelerating replacement demand across craft occupations, fashion organisations face a concrete problem: the tacit judgment of senior patternmakers leaves with them. This article sets out six approaches the industry is using to hold on to it.
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The EU AI Act imposes distinct transparency duties across multiple articles, not just Article 50. This reference maps all seven to their legal source so your compliance file cites the right provision.
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A practical, checklist-driven guide for data protection officers and AI engineers on conducting a GDPR-compliant DPIA for demand forecasting models in fashion retail, covering scoping, risk assessment, mitigation, and supervisory authority consultation.
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The EU Digital Product Passport introduces a new data layer that partially overlaps with GS1 identifiers, PIM schemas, and sustainability reporting frameworks—but conflicts with each in ways that demand deliberate migration planning. Here is where the seams are.
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Physics-based and neural learned-drape pipelines each solve garment simulation differently. This comparison helps engineers and product leads choose the right approach for their accuracy, speed, and integration requirements.
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A critical annotation of five widely cited academic papers on AI-driven fashion trend forecasting, examining methodology, data sources, and the gap between reported results and production-ready deployment.
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A catalogue of six open research datasets covering garment images, body measurements, fabric properties, and cloth simulation benchmarks, with notes on licence terms, dataset size, and the model architectures each suits best.
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A single annotated timeline of seven EU regulatory deadlines between 2026 and 2028 that every fashion compliance team needs to plan for now, covering the AI Act, Ecodesign Regulation, Digital Product Passport, and textile labelling obligations.
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Before you sign a contract with a fashion AI vendor, your procurement and legal teams need concrete answers on EU AI Act readiness. These eight questions give you a structured framework for that conversation.
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No EU authority has yet decided a case specifically against a fashion AI deployment—but the enforcement record on retail profiling, in-store biometrics, and facial-recognition scraping already sets the boundaries. Here are five transferable pitfalls, each anchored to a decided case.
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From GS1 identifiers to the CircularID Protocol and EU ESPR delegated acts, six data standards are converging on the Digital Product Passport. This guide maps each one to the DPP data model for technology architects and sustainability data leads.
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Article 10 of the EU AI Act sets concrete data governance obligations for high-risk AI systems. Here are nine controls fashion AI and data engineering teams must document now.
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A curated, annotated list of seven cloth simulation and learned-drape research papers, summarising the method, the results, and what each means for engineers and product managers building or buying fashion technology.
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Eight documented Digital Product Passport pilot programmes in the fashion and textile sector, with the data architecture decisions, stakeholder challenges, and technical lessons that brands preparing their own implementations can apply directly.
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A new report signals that AI-powered search assistants are fundamentally altering how consumers discover fashion products online, requiring brands to move beyond keyword optimisation toward structured, semantically rich content strategies.
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From 'AI system' to 'general-purpose AI model', the EU AI Act's core definitions carry precise legal meanings that differ from everyday usage. This digest corrects ten of the most common misreadings by fashion technology and legal teams.
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Industry commentary and research point to a widening gap between fashion brands' stated AI ambitions and their actual governance and data readiness. Technology strategists need to understand where the friction is concentrated.
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Aggregate metrics obscure whether a fashion AI pilot is working. This guide shows technology and business leads how to define KPIs that are specific enough to act on—scoped by operation, category, and time window—drawing on NIST's MEASURE function and RAND's research into AI project failure.
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A practical implementation guide for product, IT, and sustainability teams preparing their first Digital Product Passport for a footwear line, from identifier assignment to downstream data handover.
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A practical tutorial for technology and compliance teams on how to identify every AI component in a fashion product pipeline, assign provider and deployer obligations under the EU AI Act, and produce defensible documentation of the chain of responsibility.
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Deploying an AI virtual try-on system triggers a mandatory GDPR Data Protection Impact Assessment. This guide walks DPOs and privacy engineers through each required element, from scoping to residual-risk sign-off.
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A practical tutorial for AI engineers and compliance teams at fashion retailers: how to produce the system card and technical documentation that the EU AI Act requires for a limited-risk product recommendation engine.
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Gap Inc. established a dedicated Office of AI and a Google Cloud partnership spanning Gemini, Vertex AI and BigQuery. This analysis extracts the organisational lessons for European heads of data governance — while being explicit that Gap's public disclosures address no EU obligation.
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The AI-native versus AI-integrated framing is everywhere in PLM marketing, but no independent literature uses it as an architectural category. This article sets out what each side is actually claiming and gives architects the questions that separate rebuilt from bolted-on.
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Fashion brands integrating foundation models into product development workflows now face a distinct layer of EU AI Act obligations targeting general-purpose AI model providers and deployers. This article maps those obligations and identifies what remains unsettled.
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The EU AI Act introduces distinct conformity assessment tracks for high-risk AI systems. This primer maps those procedures to the recommendation engines and computer vision tools most commonly deployed in fashion, and explains what your technical documentation must contain.
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Luxurynsight's acquisition of Heuritech consolidates two of the most-cited AI trend intelligence platforms in luxury fashion. Technology and procurement teams need to reassess data access, model independence, and the structural risks that platform roll-ups introduce.
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The Ecodesign for Sustainable Products Regulation introduces mandatory product sustainability data that AI-based lifecycle tools in fashion must both generate and evidence. This article maps the obligations, identifies where automation helps, and flags what remains unresolved.
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AI-driven product recommendations and personalised pricing sit at a complex intersection of EU consumer law. This article maps the disclosure and fairness obligations that fashion e-commerce legal and product teams must satisfy today.
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Physical fabric digitisation already captures rich material data—but which parameters satisfy emerging Digital Product Passport requirements, and where do the gaps remain? This article maps xTex measurements against DPP mandates for material developers and sustainability data leads.
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Connecting an existing PLM system to a Digital Product Passport data layer is not a plug-and-play exercise. This article maps the architecture decisions enterprise IT leads at fashion groups must make before the EU's DPP mandates take effect.
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Retail demand forecasting AI is minimal risk under the EU AI Act—it appears nowhere in Annex III and triggers no high-risk obligations. Here is what that classification means in practice for compliance and data teams.
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The EU AI Act's extraterritorial scope means non-EU fashion brands and AI vendors serving EU markets must meet EU standards or risk exclusion. This analysis maps the compliance pressure points and where regulatory divergence creates real strategic risk.
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Training image-generation models on garment photographs and design archives creates layered legal exposure for fashion brands and AI vendors alike. This article maps the copyright, database rights, and EU AI Act documentation obligations your legal, IP, and AI product teams need to understand.
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The EU AI Act sorts AI systems into four risk tiers, each carrying distinct obligations. This guide maps the most common fashion AI use cases to those tiers and explains what compliance looks like in practice for AI governance leads and product teams.
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The EU Ecodesign for Sustainable Products Regulation is rolling out in phases, with textiles among the earliest sectors in scope. This guide maps the implementation schedule and the technical documentation obligations your product development team needs to act on now.
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Photographs and body measurements captured by virtual try-on tools are generally not special-category biometric data under GDPR — but that does not mean they are unregulated. This article sets out the correct legal framework, the narrow case where Article 9 does apply, and what data protection…
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The EU Digital Product Passport regulation will require apparel and footwear brands to expose structured product data via a machine-readable carrier. This article maps the mandatory fields, the phased-in requirements, and what your data architecture needs to handle both.
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The EU AI Omnibus reshaped the compliance calendar for fashion brands deploying recommendation, personalisation, and design-generation systems. This article maps the substantive changes and the actions your team must take before the next enforcement date.
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Before signing an AI pilot contract, fashion procurement and legal teams need a structured governance framework. This checklist covers vendor due diligence, data-processing agreements, success-metric definition, and exit clauses.
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AI proofs-of-concept in fashion organisations fail for predictable, structural reasons. This article diagnoses the root causes and gives technology leads a framework for deciding whether a stalled pilot is worth rescuing.
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A practical preparation framework for brand product and technology teams attending PI Apparel events, and for solution providers exhibiting or presenting at them.
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When a fashion enterprise builds its own internal AI platform on top of foundation models, it does not simply inherit the compliance posture of its upstream vendor. This article sets out what the EU AI Act demands of legal, compliance, and engineering leads who deploy and govern those tools.
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