pith. sign in

arxiv: 2604.05575 · v1 · submitted 2026-04-07 · 💻 cs.SE

Bias Ahead: Sensitive Prompts as Early Warnings for Fairness in Large Language Models

Gianmario Voria , Martina De Lucia , Alessandra Raia , Andrea De Lucia , Gemma Catolino , Fabio Palomba This is my paper

Pith reviewed 2026-05-10 19:35 UTC · model grok-4.3

classification 💻 cs.SE
keywords sensitive promptsfairness in LLMsbias detectionprompt sensitivityLLM evaluationethical implicationsautomated classifierpreventive design
0
0 comments X

The pith

Sensitive prompts can serve as early warnings for fairness risks in large language models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces sensitive prompts as inputs that are not biased on their own but tend to draw out responses from language models that miss ethical, relational, or contextual points. The authors built the SensY dataset of 12,801 prompts mixing synthetic and real examples across seven domains, then tested three open-source models and reviewed 4,500 answers by hand. Models usually gave factually right answers yet often skipped the implications of the sensitive topic. They also trained a classifier that predicts which prompts are sensitive with strong results on those cases. This setup lets developers spot potential fairness problems during design instead of after a model is released.

Core claim

We propose sensitive prompts as a new abstraction for fairness evaluation: inputs that are not inherently biased but are more likely to elicit biased or inadequate responses due to the sensitivity of their content. We construct and release SensY, a dataset of 12,801 prompts categorized as sensitive and non-sensitive across seven thematic domains. Querying three open-source LLMs and manually analyzing 4,500 responses shows that models often provide factually correct answers but frequently fail to acknowledge ethical, relational, or contextual implications. We develop an automated classifier for predicting prompt sensitivity that achieves robust performance, demonstrating that prompt detection

What carries the argument

Sensitive prompts, defined as inputs that are not inherently biased but more likely to elicit inadequate responses, used to detect fairness risks ahead of model use.

If this is right

  • Developers can screen prompts for sensitivity before deploying an LLM to catch bias risks early.
  • Fairness work can move from fixing problems after release to preventing them during model design.
  • An automated sensitivity classifier can be added to testing and validation pipelines for new models.
  • Models may need extra training or safeguards so they address implications when handling sensitive content.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If prompt sensitivity reliably signals bias, then filtering sensitive examples during training data creation could lower overall model bias.
  • The same detection approach could be tested on other model risks such as unsafe outputs or privacy leaks in responses.
  • Runtime checks for sensitive user queries might allow models to add extra caution or disclaimers in live applications.
  • The method assumes sensitivity can be judged without depending on any specific model's behavior, which would need checks across more model types.

Load-bearing premise

That hand review of 4,500 model responses can reliably detect when answers ignore ethical or contextual implications, and that the SensY dataset covers enough real-world situations where bias might appear.

What would settle it

If a new test set of prompts shows that the sensitivity classifier cannot distinguish cases where models give inadequate answers from cases where they do not, or if models fail equally on sensitive and non-sensitive prompts, the early-warning claim would not hold.

read the original abstract

Large Language Models (LLMs) are being increasingly integrated into software systems, offering powerful capabilities but also raising concerns about fairness. Existing fairness benchmarks, however, focus on stereotype-specific associations, which limit their ability to anticipate risks in diverse, real-world contexts. In this paper, we propose sensitive prompts as a new abstraction for fairness evaluation: inputs that are not inherently biased but are more likely to elicit biased or inadequate responses due to the sensitivity of their content. We construct and release SensY, a dataset of 12,801 prompts, categorized as sensitive and non-sensitive, spanning seven thematic domains, combining synthetic generation and real user inputs. Using this dataset, we query three open-source LLMs and manually analyze 4,500 responses to evaluate their adequacy in answering sensitive prompts. Results show that while models often provide factually correct answers, they frequently fail to acknowledge the ethical, relational, or contextual implications of sensitive inputs. In addition, we develop an automated classifier for predicting prompt sensitivity, achieving robust performance on sensitive prompts. Our findings demonstrate that prompt sensitivity can serve as an effective early-warning mechanism for fairness risks in LLMs. This perspective shifts fairness assessment from reactive mitigation toward preventive design, enabling developers to anticipate and manage bias before deployment.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes sensitive prompts—inputs not inherently biased but likely to elicit biased or inadequate LLM responses—as a new abstraction for fairness evaluation. It constructs and releases the SensY dataset (12,801 prompts across seven domains, mixing synthetic and real inputs), queries three open-source LLMs, manually reviews 4,500 responses to assess adequacy, and trains a classifier to predict prompt sensitivity. The central claim is that prompt sensitivity serves as an effective early-warning mechanism for fairness risks, shifting assessment from reactive mitigation to preventive design.

Significance. If the manual evaluation protocol is made rigorous and the dataset's representativeness is demonstrated, the work could usefully reframe fairness assessment toward proactive detection via prompt characteristics. The public release of SensY is a concrete contribution that enables follow-on work. The classifier component, if its performance metrics hold under scrutiny, offers a practical tool for developers.

major comments (2)
  1. [Evaluation section] The manual analysis of 4,500 responses (described in the evaluation section) provides no annotation protocol, decision criteria for 'failure to acknowledge the ethical, relational, or contextual implications,' sampling procedure for the 4,500 items, or inter-annotator reliability statistics. This is load-bearing for the central claim that models 'frequently fail to acknowledge...' and that sensitivity therefore functions as an early-warning signal; without these details the observed inadequacies cannot be distinguished from appropriate hedging or refusal.
  2. [Results section] No quantitative results, baselines, or performance metrics are reported for the automated classifier (mentioned in the abstract and results), only the qualitative statement 'achieving robust performance on sensitive prompts.' This leaves the practical utility of the sensitivity predictor unverified and weakens the preventive-design argument.
minor comments (2)
  1. [Abstract] The abstract states that existing benchmarks 'limit their ability to anticipate risks in diverse, real-world contexts' but provides no citations or comparison table to prior fairness benchmarks; adding these would clarify the novelty claim.
  2. [Dataset section] The SensY dataset construction (synthetic plus real inputs) is asserted to span seven domains without an accompanying coverage analysis or external validation against real-world bias-incident corpora; a brief table or appendix quantifying domain balance would improve transparency.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which identifies key areas where additional rigor will strengthen the manuscript. We address each major comment below and will incorporate the requested details in a revised version.

read point-by-point responses

  1. Referee: [Evaluation section] The manual analysis of 4,500 responses (described in the evaluation section) provides no annotation protocol, decision criteria for 'failure to acknowledge the ethical, relational, or contextual implications,' sampling procedure for the 4,500 items, or inter-annotator reliability statistics. This is load-bearing for the central claim that models 'frequently fail to acknowledge...' and that sensitivity therefore functions as an early-warning signal; without these details the observed inadequacies cannot be distinguished from appropriate hedging or refusal.

    Authors: We agree that these methodological details are necessary to substantiate the manual evaluation and distinguish sensitivity-related failures from appropriate model behavior. In the revised manuscript we will add a new subsection in the evaluation section that specifies: (1) the full annotation protocol and decision criteria (with concrete examples of responses that do vs. do not acknowledge ethical/relational/contextual implications), (2) the sampling procedure (stratified random selection across the seven domains, three models, and sensitive/non-sensitive categories), and (3) inter-annotator reliability statistics (e.g., Fleiss' kappa and pairwise agreement rates). These additions will directly support the early-warning claim. revision: yes

  2. Referee: [Results section] No quantitative results, baselines, or performance metrics are reported for the automated classifier (mentioned in the abstract and results), only the qualitative statement 'achieving robust performance on sensitive prompts.' This leaves the practical utility of the sensitivity predictor unverified and weakens the preventive-design argument.

    Authors: We concur that quantitative evidence is required to demonstrate the classifier's utility and to support the preventive-design argument. In the revision we will expand the results section with a dedicated classifier evaluation subsection that reports standard metrics (accuracy, precision, recall, F1, AUC-ROC) on held-out data, includes comparisons to baselines (e.g., TF-IDF logistic regression and a fine-tuned smaller transformer), and describes the training/validation split, feature representation, and hyper-parameter choices. This will replace the qualitative statement with verifiable numbers. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical observations and dataset construction are self-contained

full rationale

The paper constructs SensY (12,801 prompts across domains), queries three LLMs, manually reviews 4,500 responses for adequacy on ethical/relational/contextual implications, and trains an automated sensitivity classifier. No equations, derivations, or parameter-fitting steps exist that reduce any claimed result to its inputs by construction. The central claim—that sensitivity serves as an early-warning—rests on direct empirical observation rather than self-definition, fitted-input renaming, or self-citation chains. The classifier is described as achieving robust performance on the released dataset without evidence of circular evaluation loops. This is a standard empirical pipeline with no load-bearing self-referential reductions.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on domain assumptions about LLM behavior and human judgment of adequacy rather than new mathematical entities or fitted parameters.

axioms (2)
  • domain assumption LLMs produce responses to sensitive content that can be factually correct yet inadequate in ethical or contextual dimensions
    Invoked to motivate the need for sensitivity-based evaluation and the manual analysis results.
  • domain assumption Human raters can consistently identify when responses fail to acknowledge ethical, relational, or contextual implications
    Underlies the manual analysis of 4,500 responses and the claim of frequent failures.
invented entities (1)
  • sensitive prompts no independent evidence
    purpose: New abstraction for anticipating fairness risks in LLMs
    Introduced as inputs more likely to elicit biased responses due to content sensitivity, distinct from inherently biased prompts.

pith-pipeline@v0.9.0 · 5536 in / 1463 out tokens · 148527 ms · 2026-05-10T19:35:56.203224+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

26 extracted references · 26 canonical work pages

  1. [1]

    Foundation and large language models: Fundamentals, challenges, opportunities, and social impacts,

    D. Myers, R. Mohawesh, V . I. Chellaboina, A. L. Sathvik, P. Venkatesh, Y .-H. Ho, H. Henshaw, M. Alhawawreh, D. Berdik, and Y . Jararweh, “Foundation and large language models: Fundamentals, challenges, opportunities, and social impacts,”Cluster Computing, vol. 27, no. 1, p. 1–26, Nov. 2023. [Online]. Available: https: //doi.org/10.1007/s10586-023-04203-7

    work page doi:10.1007/s10586-023-04203-7 2023

  2. [2]

    Students’ perception of chatgpt in software engineering: Lessons learned from five courses,

    L. Baresi, A. De Lucia, A. Di Marco, M. Di Penta, D. Di Ruscio, L. Mar- iani, D. Micucci, F. Palomba, M. T. Rossi, and F. Zampetti, “Students’ perception of chatgpt in software engineering: Lessons learned from five courses,” in2025 IEEE/ACM 37th International Conference on Software Engineering Education and Training (CSEE&T). IEEE, 2025, pp. 158– 169

    work page 2025

  3. [3]

    Large language models for software engineering: Sur- vey and open problems,

    A. Fan, B. Gokkaya, M. Harman, M. Lyubarskiy, S. Sengupta, S. Yoo, and J. M. Zhang, “Large language models for software engineering: Sur- vey and open problems,” in2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE). IEEE, 2023, pp. 31–53

    work page 2023

  4. [4]

    A systematic evaluation of large language models of code,

    F. F. Xu, U. Alon, G. Neubig, and V . J. Hellendoorn, “A systematic evaluation of large language models of code,” inProceedings of the 6th ACM SIGPLAN international symposium on machine programming, 2022, pp. 1–10

    work page 2022

  5. [5]

    Bias and unfairness in information retrieval systems: New challenges in the llm era,

    S. Dai, C. Xu, S. Xu, L. Pang, Z. Dong, and J. Xu, “Bias and unfairness in information retrieval systems: New challenges in the llm era,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024, pp. 6437–6447

    work page 2024

  6. [6]

    Gartner predicts 75% of analytics content to use genai for enhanced contextual intelligence by 2027,

    Gartner, “Gartner predicts 75% of analytics content to use genai for enhanced contextual intelligence by 2027,” Press Release, 2025, https://www.gartner.com/en/newsroom/press-releases/2025-06- 18-gartner-predicts-75-percent-of-analytics-content-to-use-genai-for- enhanced-contextual-intelligence-by-2027

    work page 2027

  7. [7]

    Nigerian software engineer or american data scientist? github profile recruitment bias in large language models,

    T. Nakano, K. Shimari, R. G. Kula, C. Treude, M. Cheong, and K. Matsumoto, “Nigerian software engineer or american data scientist? github profile recruitment bias in large language models,” in2024 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 2024, pp. 624–629

    work page 2024

  8. [8]

    She elicits requirements and he tests: Software engineering gender bias in large language models,

    C. Treude and H. Hata, “She elicits requirements and he tests: Software engineering gender bias in large language models,” in2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR). IEEE, 2023, pp. 624–629

    work page 2023

  9. [9]

    Is attention all you need? toward a conceptual model for social awareness in large language models,

    G. V oria, G. Catolino, and F. Palomba, “Is attention all you need? toward a conceptual model for social awareness in large language models,” in Proceedings of the 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering, 2024, pp. 69–73

    work page 2024

  10. [10]

    Bias mitigation for machine learning classifiers: A comprehensive survey,

    M. Hort, Z. Chen, J. M. Zhang, M. Harman, and F. Sarro, “Bias mitigation for machine learning classifiers: A comprehensive survey,” ACM Journal on Responsible Computing, vol. 1, no. 2, pp. 1–52, 2024

    work page 2024

  11. [11]

    Fairness on a budget, across the board: A cost-effective evaluation of fairness-aware practices across contexts, tasks, and sensi- tive attributes,

    A. Parziale, G. V oria, G. Giordano, G. Catolino, G. Robles, and F. Palomba, “Fairness on a budget, across the board: A cost-effective evaluation of fairness-aware practices across contexts, tasks, and sensi- tive attributes,”Information and Software Technology, p. 107858, 2025

    work page 2025

  12. [12]

    Square: A large-scale dataset of sensitive questions and acceptable responses created through human-machine collabora- tion,

    H. Lee, S. Hong, J. Park, T. Kim, M. Cha, Y . Choi, B. Kim, G. Kim, E.-J. Lee, Y . Limet al., “Square: A large-scale dataset of sensitive questions and acceptable responses created through human-machine collabora- tion,” inProceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023, pp. 6692– 6712

    work page 2023

  13. [13]

    Online appendix

    A. Authors, “Online appendix.” [Online]. Available: https://anonymous. 4open.science/r/SensY/README.md

    work page

  14. [14]

    Fairness-aware practices from developers’ perspective: A survey,

    G. V oria, G. Sellitto, C. Ferrara, F. Abate, A. De Lucia, F. Ferrucci, G. Catolino, and F. Palomba, “Fairness-aware practices from developers’ perspective: A survey,”Information and Software Technology, vol. 182, p. 107710, 2025

    work page 2025

  15. [15]

    A review on fairness in machine learning,

    D. Pessach and E. Shmueli, “A review on fairness in machine learning,” ACM Computing Surveys (CSUR), vol. 55, no. 3, pp. 1–44, 2022

    work page 2022

  16. [16]

    Fairness percep- tions of algorithmic decision-making: A systematic review of the empiri- cal literature,

    C. Starke, J. Baleis, B. Keller, and F. Marcinkowski, “Fairness percep- tions of algorithmic decision-making: A systematic review of the empiri- cal literature,”Big Data & Society, vol. 9, no. 2, p. 20539517221115189, 2022

    work page 2022

  17. [17]

    A survey on bias and fairness in machine learning,

    N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, “A survey on bias and fairness in machine learning,”ACM Computing Surveys (CSUR), vol. 54, no. 6, pp. 1–35, 2021

    work page 2021

  18. [18]

    Fairness improvement with multiple protected attributes: How far are we?

    Z. Chen, J. M. Zhang, F. Sarro, and M. Harman, “Fairness improvement with multiple protected attributes: How far are we?” inProceedings of the IEEE/ACM 46th international conference on software engineering, 2024, pp. 1–13

    work page 2024

  19. [19]

    Identifying and reducing gender bias in word-level language models,

    S. Bordia and S. Bowman, “Identifying and reducing gender bias in word-level language models,” inProceedings of the 2019 conference of the north American chapter of the association for computational linguistics: student research workshop, 2019, pp. 7–15

    work page 2019

  20. [20]

    Dealing with popularity bias in recommender systems for third-party libraries: How far are we?

    P. T. Nguyen, R. Rubei, J. Di Rocco, C. Di Sipio, D. Di Ruscio, and M. Di Penta, “Dealing with popularity bias in recommender systems for third-party libraries: How far are we?” in2023 IEEE/ACM 20th Inter- national Conference on Mining Software Repositories (MSR). IEEE, 2023, pp. 12–24

    work page 2023

  21. [21]

    Stereoset: Measuring stereotyp- ical bias in pretrained language models,

    M. Nadeem, A. Bethke, and S. Reddy, “Stereoset: Measuring stereotyp- ical bias in pretrained language models,” inProceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 1: long papers), 2021, pp. 5356–5371

    work page 2021

  22. [22]

    Bold: Dataset and metrics for measuring biases in open-ended language generation,

    J. Dhamala, T. Sun, V . Kumar, S. Krishna, Y . Pruksachatkun, K.-W. Chang, and R. Gupta, “Bold: Dataset and metrics for measuring biases in open-ended language generation,” inProceedings of the 2021 ACM conference on fairness, accountability, and transparency, 2021, pp. 862– 872

    work page 2021

  23. [23]

    Bbq: A hand-built bias benchmark for question answering,

    A. Parrish, A. Chen, N. Nangia, V . Padmakumar, J. Phang, J. Thompson, P. M. Htut, and S. Bowman, “Bbq: A hand-built bias benchmark for question answering,” inFindings of the Association for Computational Linguistics: ACL 2022, 2022, pp. 2086–2105

    work page 2022

  24. [24]

    Empirical Standards for Software Engineering Research

    P. Ralph, S. Baltes, D. Bianculli, Y . Dittrich, M. Felderer, R. Feldt, A. Filieri, C. A. Furia, D. Graziotin, P. He, R. Hoda, N. Juristo, B. A. Kitchenham, R. Robbes, D. M ´endez, J. S. Moll ´eri, D. Spinellis, M. Staron, K. Stol, D. A. Tamburri, M. Torchiano, C. Treude, B. Turhan, and S. Vegas, “ACM SIGSOFT empirical standards,”CoRR, vol. abs/2010.03525...

    work page arXiv 2010

  25. [25]

    Chatbot arena: An open platform for evaluating llms by human preference,

    W.-L. Chiang, L. Zheng, Y . Sheng, A. N. Angelopoulos, T. Li, D. Li, B. Zhu, H. Zhang, M. Jordan, J. E. Gonzalezet al., “Chatbot arena: An open platform for evaluating llms by human preference,” inForty-first International Conference on Machine Learning, 2024

    work page 2024

  26. [26]

    A team- based approach to open coding: Considerations for creating intercoder consensus,

    M. A. Cascio, E. Lee, N. Vaudrin, and D. A. Freedman, “A team- based approach to open coding: Considerations for creating intercoder consensus,”Field Methods, vol. 31, no. 2, pp. 116–130, 2019

    work page 2019