mlsanityData to 14 Oct 2026

Trade-Offs Between Fairness and Privacy in Language Modeling

arXiv 2305.14936 ↗First posted 24 May 2023cs.CLcited by 4

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Abstract (arXiv)

Protecting privacy in contemporary NLP models is gaining in importance. So does the need to mitigate social biases of such models. But can we have both at the same time? Existing research suggests that privacy preservation comes at the price of worsening biases in classification tasks. In this paper, we explore the extent to which this tradeoff really holds when we incorporate both privacy preservation and de-biasing techniques into training text generation models. How does improving the model along one dimension affect the other dimension as well as the utility of the model? We conduct an extensive set of experiments that include bias detection, privacy attacks, language modeling, and performance on downstream tasks.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 43 of its 76 references that are in the map. First posted on arXiv 24 May 2023.

Builds on

43 of 76 references.

References · 43

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31 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 2.

4 papers.

  1. 2024Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learningcited by 9
  2. 2024Composable Interventions for Language Modelscited by 4
  3. 2026PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatizationcited by 0
  4. 2026The Privacy-Hallucination Tradeoff in Differentially Private Language Modelscited by 0