Understanding and Meeting Practitioner Needs When Measuring Representational Harms Caused by LLM-Based Systems

The NLP research community has made publicly available numerous instruments for measuring representational harms caused by large language model (LLM)-based systems.These instruments have taken the form of datasets, metrics, tools, and more.In this paper, we examine the extent to which such instruments meet the needs of practitioners tasked with evaluating LLM-based systems.Via semistructured interviews with 12 such practitioners, we find that practitioners are often unable to use publicly available instruments for measuring representational harms.We identify two types of challenges.In some cases, instruments are not useful because they do not meaningfully measure what practitioners seek to measure or are otherwise misaligned with practitioner needs.In other cases, instruments-even useful instruments-are not used by practitioners due to practical and institutional barriers impeding their uptake.Drawing on measurement theory and pragmatic measurement, we provide recommendations for addressing these challenges to better meet practitioner needs.