mlsanityData to 7 Oct 2026

Equitable Evaluation via Elicitation

arXiv 2602.21327 ↗First posted 24 Feb 2026cs.LG cs.AI cs.CYcited by 0

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Abstract (arXiv)

Individuals with similar qualifications and skills may vary in their demeanor, or outward manner: some tend toward self-promotion while others are modest to the point of omitting crucial information. Comparing the self-descriptions of equally qualified job-seekers with different self-presentation styles is therefore problematic. We build an interactive AI for skill elicitation that provides accurate determination of skills while simultaneously allowing individuals to speak in their own voice. Such a system can be deployed, for example, when a new user joins a professional networking platform, or when matching employees to needs during a company reorganization. To obtain sufficient training data, we train an LLM to act as synthetic humans. Elicitation mitigates endogenous bias arising from individuals' own self-reports. To address systematic model bias we enforce a mathematically rigorous notion of equitability ensuring that the covariance between self-presentation manner and skill evaluation error is small.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 49 references that are in the map. First posted on arXiv 24 Feb 2026.

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21 of 49 references.

References · 21

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  4. 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,414
  5. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  6. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  7. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 381
  8. 2018Multiaccuracy: Black-Box Post-Processing for Fairness in Classificationcited by 221
  9. 2020Outcome Indistinguishabilitycited by 74
  10. 2021Omnipredictorscited by 71
  11. 2020Moment Multicalibration for Uncertainty Estimationcited by 63
  12. 2022Loss Minimization through the Lens of Outcome Indistinguishabilitycited by 61
9 more
  1. 2020Two Simple Ways to Learn Individual Fairness Metrics from Datacited by 54
  2. 2019Metric Learning for Individual Fairnesscited by 51
  3. 2018Probably Approximately Metric-Fair Learningcited by 29
  4. 2024First-Person Fairness in Chatbotscited by 18
  5. 2024AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewerscited by 11
  6. 2024Active Exploration via Autoregressive Generation of Missing Datacited by 8
  7. 2025Adaptive Elicitation of Latent Information Using Natural Languagecited by 7
  8. 2025Exploring LLMs for Automated Generation and Adaptation of Questionnairescited by 3
  9. 2024Learning Interpretable Fair Representationscited by 1

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