Lune

EMNLP Findings2021

'Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP

Anna Rogers, Timothy Baldwin, Kobi Leins

摘要

A key part of the NLP ethics movement is responsible use of data, but exactly what that means or how it can be best achieved remain unclear. This position paper discusses the core legal and ethical principles for collection and sharing of textual data, and the tensions between them.

We propose a potential checklist for responsible data (re-)use that could both standardise the peer review of conference submissions, as well as enable a more in-depth view of published research across the community. Our proposal aims to contribute to the development of a consistent standard for data (re-)use, embraced across NLP conferences.

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引用它的顶会 Paper11

  1. Reproducibility in Computational Linguistics: Is Source Code Enough?Mohammad Arvan, Luís Pina, Natalie PardeEMNLP 2022 · 12 次引用
  2. Building Better: Avoiding Pitfalls in Developing Language Resources when Data is ScarceNedjma Ousidhoum, Meriem Beloucif, Saif M. MohammadACL 2025 · 5 次引用
  3. Enhancing Data Quality through Simple De-duplication: Navigating Responsible Computational Social Science ResearchYida Mu, Mali Jin, Xingyi Song, Nikolaos AletrasEMNLP 2024 · 4 次引用
  4. ECBD: Evidence-Centered Benchmark Design for NLPYu Lu Liu, Su Lin Blodgett, Jackie C. K. Cheung, Vera Liao 等ACL 2024 · 3 次引用
  5. In Benchmarks We Trust ... Or Not?Ine Gevers, Victor De Marez, Jens Van Nooten, Jens Lemmens 等EMNLP 2025 · 1 次引用

它引用的 Paper5

  1. Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah 等NeurIPS 2020 · 64,255 次引用
  2. Extracting Training Data from Large Language ModelsNicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski 等USENIX Security 2021 · 2,866 次引用
  3. Language (Technology) is Power: A Critical Survey of "Bias" in NLPSu Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna M. WallachACL 2020 · 68 次引用
  4. Use of Formal Ethical Reviews in NLP Literature: Historical Trends and Current PracticesSebastin Santy, Anku Rani, Monojit ChoudhuryACL Findings 2021 · 9 次引用
  5. Changing the World by Changing the DataAnna RogersACL 2021

相关 Paper

  1. Thorny Roses: Investigating the Dual Use Dilemma in Natural Language ProcessingLucie-Aimée Kaffee, Arnav Arora, Zeerak Talat, Isabelle AugensteinEMNLP Findings 2023 · 12 次引用
  2. Reproducibility in NLP: What Have We Learned from the Checklist?Ian Magnusson, Noah A. Smith, Jesse DodgeACL Findings 2023 · 14 次引用
  3. Is NLP Ready for Standardization?Lauriane AufrantEMNLP Findings 2022 · 4 次引用
  4. Yes-Yes-Yes: Proactive Data Collection for ACL Rolling Review and BeyondNils Dycke, Ilia Kuznetsov, Iryna GurevychEMNLP Findings 2022 · 10 次引用
  5. On the Gap between Adoption and Understanding in NLPFederico Bianchi, Dirk HovyACL Findings 2021 · 25 次引用
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