Artificial Intelligence-related digital skills and employment outcomes for underrepresented women

This study examines whether signalling AI-related digital skills improves employment outcomes for women from underrepresented groups in England, defined by race, age, sexual orientation, and autism-spectrum disclosure. Using correspondence evidence, the study finds that underrepresented women receive fewer interview invitations and are considered for lower-paid vacancies than majority-group women. In pooled analyses, signalling AI-related digital skills increases interview invitations for underrepresented applicants, but does not eliminate the disadvantage. These findings are consistent with AI Capital and productivity-signalling frameworks, as employers appear to value AI-related capabilities while the returns to such credentials remain constrained by persistent demographic inequalities. The study therefore shows that positive returns to AI-related skills and labour-market disadvantage can coexist. Its broader implication is that digital upskilling can strengthen the recruitment prospects of underrepresented women, but cannot by itself deliver parity in employment outcomes. A dual policy response is therefore required, combining wider and more equitable access to AI education and training with stronger anti-discrimination enforcement, greater transparency in shortlisting, improved oversight of recruitment processes, and systematic evaluation of how employers recognise applicant credentials. The study contributes to the economics of AI by linking correspondence evidence to the AI Capital framework and highlighting barriers to converting AI-related resources into meaningful employment opportunities.