Ethical Challenges of Artificial Intelligence in Fashion Recommendation

Abstract

Artificial intelligence enabled fashion recommendation engines are increasingly informing consumers’ encounters with garments, styles, brands, sizes, and combinations of these in digital shopping. These recommendation engines can utilize browsing history, purchase history, visual features, contextual signals, body-related attributes, and behavioral cues to enhance product search and reduce information overload. The proliferation of these recommenders presents emerging ethical dilemmas that stem from algorithmic stereotyping, privacy-preserving but information-intensive profiling, diminished consumer agency, body-conscious stress, opaque personalization, commercial manipulation, provider-centered unfairness, and environmentally destructive shopping behavior.

This research introduces an Ethical Fashion Recommendation Governance Framework and tests the framework’s implications on a publicly reproducible simulation of 1,200 sessions of utility-only, explainable, fairness-aware, and responsible-AI recommenders. The utility-only configuration achieved the highest simulated relevance score (0.864 out of 1.0), but the greatest choice diversity (73.9/100) as well as the greatest consent-comprehension score (80.6/100). The responsible-AI recommender maintained a relevance score of 0.826 while achieving the best choice-diversity score (73.9/100) and the best consent-comprehension score (80.6/100). The corresponding reduction in intrusive-profiling points from 80.6/100 to 7.3/100 resulted in the greatest increase in sustainable-item total points (65.7/100). Logistic regression revealed that an increase in personalized search degrees increased modeled odds of  

ethical risk, then that data-minimization, explainability, diversity controls, and sustainability-serious reranking exerted significant countervailing effects. A supplementary synthetic recommender audit demonstrated a strong negative correlation between personalization intensity and recommendation diversity. These results reveal that ethical fashion recommendation optimization requires multiple simultaneous tradeoffs, not the maximization of only consumer interest engagement or sales conversions. Responsible implementation should empirically include considerations for provider fairness, user agency, explainability, user privacy-minimization, body-positive recommender designs, sustainable recommendation objectives, manipulative guidelines, and continuous audits.