Ethical AI in Quick-Commerce Marketing: A Framework for Sustainable and Transparent Practices

The rapid growth of quick-commerce platforms has revolutionized consumer shopping behavior by offering unmatched speed, convenience, and AI-driven personalization. This study investigates consumer perceptions of AI-driven marketing techniques in quick-commerce, focusing on pricing algorithms, promotional fairness, and data privacy. Through a structured survey, data was collected from 70 respondents, categorized by demographics and attitudes towards AI recommendations. The findings highlight that consumers prioritize speed but express concerns over AI transparency and dynamic pricing. Statistical tests, including a Chi-Square test, regression analysis, and reliability testing (Cronbach’s Alpha), reveal no significant relationship between demographics and AI fairness perceptions, confirming the robustness of the collected data. Recommendations include enhancing transparency in AI decision-making, clearer disclosure of pricing mechanisms, and strengthening consumer data privacy practices. The study contributes to ongoing discussions on ethical AI practices, emphasizing the need for a responsible, transparent, and consumer-centric approach in AI applications in quick-commerce marketing Keywords—Ethical AI, Quick-Commerce Marketing, Data Privacy, Algorithmic Bias, Transparency, Consumer Trust, AI Fairness, Personalized Pricing, Survey Analysis, Marketing Ethics