Abstract
This chapter offers a socio-technical blueprint for algorithmic nudging in fashion platforms. It defines nudging as the data-driven orchestration of choice architecture that steers customers toward lower-impact products, slower shipping, and fewer returns, while preserving autonomy and fairness. The chapter outlines a layered architecture that spans data, modeling, policy, and interface actions, linking interventions to psychological mediators such as salience, perceived consumer effectiveness, trust, and reactance. It formalizes governance through transparency, explainability, opt-out options, fairness screens, and human oversight. A measurement and experimentation program specifies primary, perceptual, and composite KPIs, including Carbon per Conversion, Impact@K, FairExposure, and the Autonomy Cost Index, along with A/B tests, uplift targeting, contextual bandits, and off-policy evaluation. Practical thresholds and reporting cadences support safe operation. The chapter closes with sectoral guidance, limitations, and implications for policymakers and practitioners.