From Stereotypes to Strategy: Addressing Gender Bias in AI-Powered Marketing

Abstract

Gender stereotyping and discrimination have long been embedded in advertising and marketing practices. Although progress has been made, it remains slow and uneven. As artificial intelligence (AI) becomes increasingly central to marketing, optimizing workflows and personalizing content, it also introduces new ethical risks. Algorithmic bias can reinforce existing social stereotypes and systematically disadvantage marginalized groups. This study investigates how marketing professionals perceive bias in AI applications and the strategies they employ to mitigate it. Drawing on qualitative expert interviews with communications managers from agencies and companies, our findings reveal a wide spectrum of awareness: while some view bias primarily as a reputational risk, others recognize it as a profound social issue. Inclusive communication is understood both as a moral obligation and a strategic choice yet often lacks institutional support or systematic evaluation mechanisms. Our analysis highlights that bias is not solely a technical flaw in data or models but can be structurally embedded in creative processes. Interviewees identify team diversity, collaborative feedback loops, critical prompting, and institutionalized spaces for reflection as key practices for fostering inclusivity. However, constraints such as limited time, budget, and organizational commitment frequently hinder consistent implementation. We argue that bias management must be integrated into a broader, ethically reflective marketing strategy. Only through the deliberate convergence of technical, creative, and ethical competencies can AI be harnessed to promote socially responsible and inclusive marketing.