First-pass research briefing, not a final academic review. Always read the original paper before citing.
Paper A
From Stereotypes to Strategy: Addressing Gender Bias in AI-Powered Marketing
Caterina Fox, Gabriele Schuster — 2026 — International Conference on Gender Research
peer reviewed journal article · · deep dive
https://doi.org/10.34190/icgr.9.1.4631
Key findings
- Marketing professionals have very different levels of awareness about AI bias — some see it mainly as a PR risk to their brand, while others see it as a serious social harm that needs to be actively addressed.
- AI tools used in marketing (like image generators or ad targeting systems) regularly reinforce gender stereotypes — for example, generating images of white families when asked for 'happy family,' or steering job ads for technical roles toward men. These patterns show up even when no one intended them.
- Bias in marketing AI doesn't just come from bad data or flawed algorithms — it also gets baked in during the creative process itself, through uncritical prompting, homogeneous teams, and a lack of structured review.
- The practices that help most — having diverse teams, building in feedback loops, being thoughtful about how you write prompts, and setting aside time for ethical reflection — are frequently skipped because of time pressure, tight budgets, and limited organizational commitment.
Marketing implications
- When using AI image or copy generators, write your prompts with explicit diversity instructions (e.g., specify different genders, races, ages, and body types). Don't rely on defaults — the defaults are often biased.
- Before publishing AI-generated campaign content, do a quick bias check: look at the images and copy and ask whether they reinforce tired stereotypes (only women in domestic roles, only men in technical roles, only white faces). This takes 10 minutes and can prevent real reputational damage.
- If your team is reviewing AI-generated content, make sure the review group includes people from different backgrounds — a homogeneous team is more likely to miss bias that feels 'normal' to everyone in the room.
AI & Marketing Research Radar — Big Plans Media — 2026-05-15
