ClearCart
Description
The rapid integration of machine learning within e-commerce has optimised personalised recommendations but inadvertently cultivated an 'algorithmic transparency paradox'. Consumers routinely encounter opaque, black-box algorithms alongside compliance-driven, binary privacy mechanisms (such as cookie banners) that extract behavioural data without offering direct financial compensation. To navigate these interconnected challenges, this study introduces 'ClearCart', a novel e-commerce architecture featuring two primary interventions. First, an 'Algorithmic Nutrition Label' utilises SHAP values to visually demystify recommendation logic. Second, a 'Data Dividend' acts as a dynamic pricing engine, operationalising Privacy Calculus Theory by providing real-time financial discounts in exchange for data disclosure.