Responsible AI in Retail Advertising: Balancing Revenue Optimization with Fairness, Transparency, and Trust

Abstract

The emergence of artificial intelligence in retail advertising has allowed the optimization of earnings on an unprecedented scale, and raises a serious ethical concern: the fairness, transparency, and consumer trust at the same time. This article will discuss the conflict between the efficiency of algorithms and the responsible implementation of AI in retail advertising practices, and how machine learning systems may unintentionally reproduce biases and discriminatory results even with a high level of technical complexity. The discussion has discussed several aspects of ensuring fairness in advertising algorithms, such as demographic parity, equalized odds, and individual fairness, and has admitted that it is mathematically impossible to have all fairness dimensions met at the same time. Explainability and transparency appear to be the main features of compliance with the regulations and consumer trust, but explainability is not enough when there is no clear communication plan to address the interests of the various stakeholder groups. To establish sustainable trust, it is necessary to integrate technical protection mechanisms like the differentiation of privacy and federated learning with effective organizational governance infrastructure, including ethics committees, human-in-the-loop and consumer control. The article provides useful implementation models including data collection, model development, deployment architecture, and post-deployment governance, as examples of how ethical AI practices are not only compliance requirements but competitive advantages. Companies that are able to effectively incorporate the issue of fairness in optimization goals have the potential to attain excellent long-term business performance and fulfill the growing demands of society to hold technology use in business responsibly.