Generative AI is transforming the way fashion brands and advertisers engage with consumers. From styling assistants that suggest complete looks to dynamic ad creatives tailored to individual preferences, the promise of AI-driven personalization is compelling. But this innovation comes with a challenge: balancing highly personalized experiences with the fundamental right to privacy.

In fashion, personalized recommendations often draw on data such as purchase history, browsing patterns, or inferred preferences like style and lifestyle. Used responsibly, this can enhance discovery and improve the user experience. However, without clear safeguards, generative AI systems may risk reinforcing stereotypes or creating perceptions of intrusive profiling, reinforcing stereotypes, perpetuating bias, eroding consumer trust or creating concerns related to intellectual property rights. There may also be the necessity to navigate regulations concerning treatment of sensitive personal data, where biometric information is involved in respect of propositions like virtual-try-on.

This is why responsible practices are critical — not only to preserve consumer trust, but also to align with evolving global standards. Emerging frameworks such as the White House Executive Order on AI, the NIST AI Risk Management Framework, and the EU AI Act are helping guide organizations toward safe, fair, and transparent use of AI in consumer experiences. Article 53 of EU AI Act require providers of general-purpose AI models to implement state-of-the-art technology to identify and respect any data mining opt-out expressed in accordance with the provisions of the Copyright Directive; in short, compliance is the need of the hour.

Key aspects of ethical AI in fashion include:

• Fair AI systems which do not create bias related to body image, culture, or demographics etc.
• Respecting consumer data privacy and maintaining transparency
• Maintaining human oversight to balance AI-driven efficiency with human judgment
• Maintaining secure systems
• Establishing robust governance programs

This article explores how organizations can implement robust AI governance which embody privacy-centric approaches and ethics to unlock the benefits of personalization while respecting consumer autonomy in AI commerce propositions. While the AI environment is still evolving, trust in AI can be established through rigorous ethical standards (embodying privacy, inclusiveness, fairness, transparency, accountability, security etc.), robust data protection measures, and a transparent AI governance framework. Some of these aspects are dealt with in further detail later in this article.

Key Privacy-First Design Principles

Event though, we, at Glance, are a deployer of "general purpose" AI models, and these models typically fall within limited or minimal risk category as they are applied in the contexts such as entertainment, commerce and content discovery, we strive to embed privacy into every stage of how AI recommendations are designed, tested, and delivered. Our framework is guided by four core tenets: minimize data, maximize transparency, empower consumers, and ensure accountability.

Data Minimization

• Collect only the minimum data required to generate relevant fashion recommendations;
• Use occasion-based signals (e.g., "workwear," "festive," "casual") instead of personal identifiers;
• Regularly review data collection to ensure no "scope creep" beyond intended use.

Transparency & Explainability

• Obtain consent for processing personal (or sensitive personal) data;
• Notify user about the third parties involved; cross border data transfers;
• Provide users with a simple explanation of why they are seeing a specific recommendation;
• Use "Why this recommendation?" pop-ups or labels for clarity;

User Choice & Control

• Process personalization locally on user devices whenever possible;
• Offer easy opt-out options from generative AI recommendations, without restricting access to standard browsing/shopping features;
• Provide preference dashboards so users can refine or reset their personalization profile;
• Ensure users can exercise their rights such as deleting their data and having visibility into how it's being used.

Accountability

• Maintain audit trails for AI-generated recommendations to support accountability and compliance;
• Subject high-risk recommendation models to review by an internal AI Governance Committee;
• Conduct regular privacy impact assessments (PIAs) on new features and AI Risk assessments, where applicable, before launching.
• Continuously monitor outputs to prevent reinforcing stereotypes or exclusionary fashion norms.

Governance and Oversight

Generative AI in fashion recommendations introduces new opportunities — but also new responsibilities. Unlike traditional ad targeting, generative systems do not simply select from predefined options; they create dynamic outputs that evolve over time. This makes strong governance structures essential to ensure that recommendations remain ethical, compliant, and aligned with consumer expectations.

Without clear accountability, generative AI can merge into areas of risk: over-personalization, biased outputs, or unintended use of sensitive data. Governance provides the controls that keep innovation both responsible and auditable, below are the core governance principles Glance follows:

• Categorize generative AI systems by risk level.
• Align AI model development with global regulations (GDPR, CCPA, DPDP) and internal privacy policies.
• Conduct Privacy Impact Assessments (PIAs) before deploying new recommendation features.
• Establish an AI Governance Committee including representatives from product, legal and information security teams.
• Document decisions on model design, deployment, and updates for internal audit and external review.
• Maintain logs of recommendation inputs, model versions, and outputs.
• Ensure auditors and regulators can reconstruct how a given recommendation was generated.
• Monitor model performance and outputs for drift, bias, and unintended consequences.
• Schedule periodic reviews of governance controls to adapt to evolving regulatory and ethical expectations.

At Glance, by embedding principles such as data minimization, on-device processing, transparency of AI outputs, and clear opt-out mechanisms, we, aspire to move from hyper-personalization toward trusted curation. Our Glance AI app/ feature leverages generative AI to create AI generated images of the users, which helps them visualize themselves in a particular attire. Consumers can then proceed to buy the items with more confidence. The AI Looks app/ feature also understands user preferences and suggests similar items to choose from; hence shopping becomes surer and more convenient from the comfort of your own home. By embedding governance into the core of AI operations, we are shifting from reactive compliance to proactive accountability.

The Future of Privacy-Respecting Fashion AI

The evolution of generative AI in fashion is only just beginning. As algorithms become more powerful and consumer expectations for personalization grow, the challenge will be to ensure that innovation does not come at the cost of privacy, fairness, or trust. The future of AI-driven fashion recommendations will be defined not by how much data is collected, but by how responsibly data is used.

Emerging Directions

• Federated & On-Device Learning: AI models will increasingly shift away from centralized data collection, instead learning directly on user devices or through privacy-preserving federated approaches.
• Synthetic & Anonymized Data: Training future models will rely more heavily on synthetic datasets that replicate consumer behavior without exposing real identities.
• Explainability by Design: Explainability will evolve from an add-on feature into a default design principle, making recommendations interpretable for consumers, advertisers, and regulators.
• Ethical Standardization: Industry-wide codes of conduct and certification schemes will emerge, defining what "responsible personalization" means in practice.

Our goal is not only to comply with privacy laws but to set higher standards for ethical AI in fashion advertising. Our commitment towards overall trust, integrity and compliance can be discovered further in our Trust Center which showcases our products, their features, privacy practices as validated by external advisors who are celebrated names in the privacy sector: https://glance.com/trust-center