Advancing Data Equity: Practitioner Responsibility and Accountability in NLP Data Practices
It addresses the problem of data equity in NLP for practitioners and policymakers, but is incremental as it builds on existing bias and governance research.
This study investigated how NLP data practitioners in the U.S. perceive and handle data equity issues, revealing tensions between commercial goals and equity commitments, and advocating for structural governance reforms to support practitioner agency.
While research has focused on surfacing and auditing algorithmic bias to ensure equitable AI development, less is known about how NLP practitioners - those directly involved in dataset development, annotation, and deployment - perceive and navigate issues of NLP data equity. This study is among the first to center practitioners' perspectives, linking their experiences to a multi-scalar AI governance framework and advancing participatory recommendations that bridge technical, policy, and community domains. Drawing on a 2024 questionnaire and focus group, we examine how U.S.-based NLP data practitioners conceptualize fairness, contend with organizational and systemic constraints, and engage emerging governance efforts such as the U.S. AI Bill of Rights. Findings reveal persistent tensions between commercial objectives and equity commitments, alongside calls for more participatory and accountable data workflows. We critically engage debates on data diversity and diversity washing, arguing that improving NLP equity requires structural governance reforms that support practitioner agency and community consent.