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The deployment of artificial intelligence (AI) holds great promise for the economy and society. However, it also brings forth risks concerning social equality, privacy, fairness, and accountability. Recent studies have shed light on significant race and gender biases in algorithmic systems, revealing how AI can negatively impact marginalized groups. Surprisingly, the category of age, crucial for social inclusion and equality in aging societies, has been largely overlooked in research and policy on AI bias.

This project addresses related questions about inequality in labor markets resulting from gender, race, and family status in the United States and Europe. Firstly, I analyze (with Profs. Michelle J. Budig and Melissa Hodges) racial and gender differences in the effects of educational attainment on wages in the United States. Importantly, we look at various education specifications and use regression decomposition and wage return models with race and gender interactions to explore group differences in educational attainment and its impact on pay gaps. Group differences in educational attainment explain some of the race pay gap, but none of the gender pay gap.