Enduring Disparities in the Workplace: A Pilot Study in the AI Community
This study exposes systemic inequities in AI/ML workplaces, particularly affecting disabled professionals and multiply marginalized groups (e.g., intersecting race × gender × disability) across belonging, accessibility, microaggressions, compensation, and well-being. Method: A mixed-methods investigation surveyed 1,260 AI/ML practitioners (academia and industry) via anonymous questionnaires, intersectional stratified analysis, qualitative thematic coding, and statistical significance testing. Contribution/Results: Over 60% of respondents experienced microaggressions; disabled professionals reported significantly worse outcomes across all measured dimensions; only 32% perceived existing DEI initiatives as effective—revealing a critical implementation–impact gap; and accessibility emerged as the most urgent unmet need. This is the first empirical, intersectionally grounded, multi-dimensional assessment of workplace equity in AI/ML. The findings establish an evidence-based benchmark and actionable framework to advance equitable DEI policy and practice in the field.