How to write an AI ethics policy for a fashion business

Fashion businesses deploying AI for product imagery, demand forecasting, virtual try-on, or customer service need a written ethics policy that sets boundaries, assigns responsibility, and ensures compliance. The EU AI Act entered into force on 1 August 2024 and most obligations take effect on 2 August 2026, whilst New York's disclosure law has applied since 9 June 2026, requiring brands to disclose AI-generated synthetic performers in advertising or face fines of 1,000 to 5,000 dollars per violation. An AI ethics policy translates these legal requirements and broader principles into operational rules that teams can follow.

What should an AI ethics policy cover?

An effective policy addresses four components: principles, risk classification, governance structure, and disclosure requirements. Start with a short statement of principles that reflects company values and regulatory expectations. H&M Group built a Responsible AI framework based on nine principles: AI should be focused, beneficial, fair, transparent, governed, collaborative, reliable, respecting human agency, and secure. These principles guide decision-making when teams evaluate new AI projects or tools.

Key ethical issues in fashion AI include privacy concerns, accountability in decision-making, transparency and explainability in AI systems, and risks of bias and discrimination in algorithms. The policy should state how the business will address each area, for example through data governance standards, bias testing, or human oversight requirements.

How do you classify AI systems by risk?

The EU Artificial Intelligence Act is now law across the bloc and establishes a risk-based framework. Unacceptable risk systems, such as manipulative or exploitative applications and biometric categorisation using sensitive traits, are banned, with non-compliance drawing fines up to 35 million euros or 7 per cent of global turnover. High-risk systems include those used in employment, worker management, or biometric identification, whilst limited-risk systems require transparency only.

Most fashion and retail use cases, such as demand forecasting, pricing, allocation, and product analytics, fall into minimal or limited risk, where the practical duties are transparency, documentation, and AI literacy, whilst use cases touching employees, biometrics, or emotion inference sit in higher-risk categories and require formal assessment before deployment. The policy should include a clear process for classifying new systems and escalating high-risk cases.

Who is accountable for AI governance?

Governance fails when no individual owns decisions. Organisations must designate specific individuals or committees, typically spanning security, risk, compliance, legal, and technology, responsible for AI governance, and when an AI system makes an error or creates harm, accountability structures ensure someone owns the remediation and learning process. McKinsey found 28 per cent of organisations now say their CEO is directly responsible for generative AI governance in 2025, double the figure from a year earlier.

The policy should name roles and decision rights. Who approves new AI tools? Who reviews bias audits? Who handles customer complaints about AI-generated content? Best practice is to ratify a one-page AI policy at the board or council level stating what AI uses are off-limits, who decides on new use cases, and what risk threshold triggers escalation, with one page as the limit. A longer implementation guide can sit behind the policy, but executive accountability must be visible.

What disclosure rules apply to AI-generated content?

On 2 August 2026, the EU AI Act's transparency rules started to apply to the AI-generated imagery brands publish every day. Article 50 requires the provider of a generation system to mark synthetic images so they are detectable as artificially generated, and requires the deployer who publishes a deepfake to disclose that the content was artificially generated or manipulated, with those transparency duties applying from 2 August 2026. Penalties reach up to 15 million euros or 3 per cent of worldwide turnover.

The obligation to label sits with the brand publishing the image, not with the AI tool, and not with the platform. The policy should specify where and how disclosure appears. In May 2025, the Advertising Standards Authority issued guidance clarifying that AI use in advertising must be disclosed when it could mislead consumers about authenticity or performance, and the ASA encourages clear contextual language such as captions or taglines explicitly identifying AI-generated elements. H&M officially announced its AI-generated digital twins in July 2025 with a Responsible AI framework including diversity audits and human oversight, whilst Levi's clarified that AI models would supplement, not replace, human models following initial backlash.

How should the policy be reviewed and updated?

AI technology and regulation are moving faster than annual policy cycles. Organisations deploying autonomous or semi-autonomous AI should shift away from approve-and-forget governance and instead implement mechanisms that track system behaviour over time, logging actions, monitoring performance drift, and reviewing outcomes periodically, which allows teams to intervene early and prevents governance concerns from surfacing only after an incident, with a continuous process mandated in this environment of constantly evolving legislation, litigation interpretations, and an enormous volume of new and adaptive applications.

The policy document itself should carry a review date, typically quarterly or twice yearly. H&M Group evaluates all AI projects using a Checklist for Responsible AI, which helps teams identify and discuss different types of potential risks and ways to mitigate them, and also ensures that the development and use of AI align with company values. Build review into the workflow, not as a separate compliance exercise. Track which AI systems are in production, who owns them, and what risks they present. A register of AI use cases with owner, data, and risk class is the first practical step.

What resources support policy development?

NIST AI RMF is the operational playbook whilst ISO 42001 is the certifiable management system, and both should be used. AI governance framework is the structured combination of NIST AI RMF functions (govern, map, measure, manage), six operational pillars (policy, inventory, model cards, monitoring, audit trail, remediation), regulatory alignment (EU AI Act, ISO 42001, state laws), and named human accountability per AI system, integrated into the organisation's existing strategic execution discipline. Participants in fashion AI research noted the absence of shared ethical standards addressing cultural representation, transparency, inclusivity, and accountability in fashion AI systems, and collaborative dialogue among fashion organisations, AI developers, researchers, policymakers, and educational institutions can facilitate the development of context-sensitive ethical frameworks for AI adoption.

In McKinsey and Business of Fashion's State of Fashion 2026, more than 35 per cent of fashion executives report they already use generative AI in daily operations and now rank AI as the single biggest opportunity facing the industry, whilst 92 per cent of fashion organisations say they will increase generative AI investment this year. The companies scaling AI successfully are those that treated governance as infrastructure, not paperwork.