What question did this study set out to answer?

The aim is to redefine progress in NLP by incorporating aspects of responsibility such as fairness and transparency.

July 1, 2026Open Access

Responsible NLP: Why performance is no longer enough

Key Points

The aim is to redefine progress in NLP by incorporating aspects of responsibility such as fairness and transparency.
Introduces the concept of Responsible NLP as a guiding framework.
Discusses the integration of multiple ethical considerations across the life cycle of language technologies.
Critiques traditional performance metrics in light of societal impact.
Highlights the need for NLP systems that consider their impact on knowledge and public trust.
Argues that responsibility should drive innovation rather than constrain it.
Calls for future research to assess the implications and accountability of NLP technologies.

Abstract

Abstract Natural language processing (NLP) has moved from a specialized research field into the everyday infrastructure of writing, search, translation, education, journalism, public administration, and scientific work. This transition changes what counts as progress. Accuracy, fluency, and benchmark performance remain important, but they are no longer sufficient when language technologies shape knowledge, decisions, identities, and public trust. This column introduces Responsible NLP as a research orientation that integrates fairness, transparency, privacy, safety, cultural diversity, environmental awareness, and human agency across the full life cycle of language technologies. It argues that responsibility is not an external constraint on innovation, but a condition for meaningful and trustworthy innovation. Future research must therefore ask not only whether an NLP system works but also for whom it works, under which assumptions, with what risks, and with what forms of accountability.

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