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How the race and gender diversity of team members is related to innovative science and technology outcomes is debated in the scholarly literature. Some studies find diversity is linked to creativity and productivity, other studies find that diversity has no effect or even negative effects on team outcomes. Based on a critical review of the literature, this paper explains the seemingly contradictory findings through careful attention to the organizational contexts of team diversity. We distinguish between representational diversity and full integration of minority scientists. Representational diversity, where organizations have workforces that match the pool of degree recipients in relevant fields, is a necessary but not sufficient condition for diversity to yield benefits. Full integration of minority scientists (i.e., including women and people of color) in an interaction context that allows for more level information exchange, unimpeded by the asymmetrical power relationships that are common across many scientific organizations, is when the full potential for diversity to have innovative outcomes is realized. Under conditions of equitable and integrated work environments, diversity leads to creativity, innovation, productivity, and positive reputational (status) effects. Thus, effective policies for diversity in science and engineering must also address integration in the organizational contexts in which diverse teams are embedded.

Gender scholars use the metaphor of the “glass escalator” to describe a tendency for men in women-dominated workplaces to be promoted into supervisory positions. More recently, scholars, including the metaphor’s original author, critique the glass escalator metaphor for not addressing the intersections of gender with other relevant identities or the ways that work has changed in the twenty-first century. I apply an intersectional lens to understand how gender and race shape women’s career paths in tech work, where twenty-first century changes to the organization of workplaces are common. I build on theories of raced and gendered labor and the glass escalator to make sense of women’s careers in a contemporary field dominated by men. I find some evidence that white women, but not women of color, experience something similar to a “glass escalator” where they are promoted into management, but those promotions are a smaller step up—more step stool than escalator. These promotions move women out of technical positions and towards business and management, releasing engineering teams from the pressure to fully incorporate women.

This article outlines a research agenda for a sociology of artificial intelligence (AI). The authors review two areas in which sociological theories and methods have made significant contributions to the study of inequalities and AI: (1) the politics of algorithms, data, and code and (2) the social shaping of AI in practice. The authors contrast sociological approaches that emphasize intersectional inequalities and social structure with other disciplines’ approaches to the social dimensions of AI, which often have a thin understanding of the social and emphasize individual-level interventions. This scoping article invites sociologists to use the discipline’s theoretical and methodological tools to analyze when and how inequalities are made more durable by AI systems. Sociologists have an ability to identify how inequalities are embedded in all aspects of society and to point toward avenues for structural social change. Therefore, sociologists should play a leading role in the imagining and shaping of AI futures.

This study advances understanding of gender pay gaps by examining organizational variation. The gender pay gap literature supplies mechanisms but does not attend to organizational variation; the gender and science literature provides insights on the role of masculinist culture in disciplines but misses pay gap mechanisms. A data set of federal workers allows comparison of men and women in the same jobs and workplaces. Agencies associated with traditionally masculine (engineering, physical sciences) and gender-neutral (biological, interdisciplinary sciences) fields differ. Pay-gap mechanisms vary: human capital differences explain a larger share in gender-neutral agencies, while at male-typed agencies men are frequently paid more than women within the same job. Although beyond the federal workers' standardized pay scale, some interdisciplinary agencies more often pay men off grade, leading to higher earnings for men. Our theory of organizational variation helps explain local agency variation and how pay practices matter in specific organizational contexts. 1 We are grateful for feedback from colleagues at our institutions on various drafts of this article and to colleagues who commented on draft versions presented at the annual American