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        <title>RISE-NLP: Interactive Residual Distribution Diagnostics for Fairness Auditing in Text Classification</title>
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    <abstract>Fairness metrics such as demographic parity and equalized odds summarize group-level behavior, while the location of subgroup errors in the score distribution often remains unclear. RISE-NLP is a diagnostic interface for auditing probabilistic text classifiers through signed residual distributions. Given prediction scores, labels, group metadata, and text, RISE-NLP plots group residual-quantile curves, reports distributional metrics, and links selected percentile regions to the examples behind the gap. Using BERT and RoBERTa predictions, we observe F_dist=0.097\text–0.150 in the two identity-linked toxicity settings and 0.055\text–0.058 in a randomly grouped SST-2 control used as a descriptive reference. A 16-participant formative study evaluates users’ understanding and perceived usefulness of the residual visualizations, showing their potential to support reasoning about fairness–accuracy trade-offs. The project is available at \hrefhttps://ufdatastudio.com/projects/risenlphttps://ufdatastudio.com/projects/risenlp.</abstract>
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%0 Conference Proceedings
%T RISE-NLP: Interactive Residual Distribution Diagnostics for Fairness Auditing in Text Classification
%A Chen, Ray
%A Kurian, Kevin
%A Grant, Christan
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F chen-etal-2026-rise
%X Fairness metrics such as demographic parity and equalized odds summarize group-level behavior, while the location of subgroup errors in the score distribution often remains unclear. RISE-NLP is a diagnostic interface for auditing probabilistic text classifiers through signed residual distributions. Given prediction scores, labels, group metadata, and text, RISE-NLP plots group residual-quantile curves, reports distributional metrics, and links selected percentile regions to the examples behind the gap. Using BERT and RoBERTa predictions, we observe F_dist=0.097\text–0.150 in the two identity-linked toxicity settings and 0.055\text–0.058 in a randomly grouped SST-2 control used as a descriptive reference. A 16-participant formative study evaluates users’ understanding and perceived usefulness of the residual visualizations, showing their potential to support reasoning about fairness–accuracy trade-offs. The project is available at \hrefhttps://ufdatastudio.com/projects/risenlphttps://ufdatastudio.com/projects/risenlp.
%U https://aclanthology.org/2026.emnlp-demos.11/
%P 120-127

Markdown (Informal)

[RISE-NLP: Interactive Residual Distribution Diagnostics for Fairness Auditing in Text Classification](https://aclanthology.org/2026.emnlp-demos.11/) (Chen et al., EMNLP 2026)

• RISE-NLP: Interactive Residual Distribution Diagnostics for Fairness Auditing in Text Classification (Chen et al., EMNLP 2026)

ACL

• Ray Chen, Kevin Kurian, and Christan Grant. 2026. RISE-NLP: Interactive Residual Distribution Diagnostics for Fairness Auditing in Text Classification. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 120–127, Budapest, Hungary. Association for Computational Linguistics.