Advancing Data Equity: Practitioner Responsibility and Accountability in NLP Data Practices

TL;DR

This paper explores NLP practitioners' perspectives on data equity, highlighting their challenges, perceptions, and the need for structural governance reforms to enhance fairness and accountability in NLP data practices.

Contribution

It centers practitioners' views on NLP data equity, linking their experiences to governance frameworks and proposing participatory recommendations for improved accountability.

Findings

Practitioners face tensions between commercial goals and equity commitments.

There is a call for more participatory and accountable data workflows.

Practitioners recognize systemic constraints impacting data fairness.

Abstract

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.