The intersection of inclusion with artificial intelligence has been a hot topic as of late, with bias, state-mandated crackdowns and demographic breakdowns of tech adoption coming to the forefront. Talent advisory firm Seramount recently put out a framework for leading inclusively when it comes to AI, with five key tenets:
- Build AI fluency.
- Embed inclusion where AI decisions are made.
- Make the learning curve more equitable.
- Define human accountability early.
- Put AI to work in moments that matter.
The directive to embed inclusion in AI decisions is backed up by recent reports. For example, research has shown that although women are driving a lot of the engineering and research around AI, their career trajectories tend to wane over time — receiving 30% less visibility than their male colleagues, according to a 2024 report by Zeki.
Likewise, two 2025 reports from Randstad outlined that, in the general worker population, more men say they’re skilled in AI than women: Seventy-one percent of men surveyed by Randstad said so, compared to 29% of women respondents. And further, an April report from the National Partnership for Women and Families suggests that while women make up a little less than half of the workforce, they make up 83% of people in artificial intelligence-vulnerable roles at work.
The same NPWF report also noted that Black and multiracial women made up an even larger share of AI-vulnerable workers than their White peers. This finding from the women’s advocacy organization is far from the first conversation about the racial disparities brought forth by AI: Leadership at Color of Change previously outlined how AI negatively affects Black and brown people environmentally as well as financially.
AI discrimination conversations are also touching older workers, with experts discussing the legal and cultural implications of the AI-related class-action lawsuit against Workday, alleging a violation of the Age Discrimination in Employment Act.
