mlsanityData to 14 Oct 2026

Practising responsibility: Ethics in NLP as a hands-on course

arXiv 2512.24825 ↗First posted 31 Dec 2025cs.CL cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

As Natural Language Processing (NLP) systems become more pervasive, integrating ethical considerations into NLP education has become essential. However, this presents inherent challenges in curriculum development: the field's rapid evolution from both academia and industry, and the need to foster critical thinking beyond traditional technical training. We introduce our course on Ethical Aspects in NLP and our pedagogical approach, grounded in active learning through interactive sessions, hands-on activities, and "learning by teaching" methods. Over four years, the course has been refined and adapted across different institutions, educational levels, and interdisciplinary backgrounds; it has also yielded many reusable products, both in the form of teaching materials and in the form of actual educational products aimed at diverse audiences, made by the students themselves. By sharing our approach and experience, we hope to provide inspiration for educators seeking to incorporate social impact considerations into their curricula.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 35 references that are in the map. First posted on arXiv 31 Dec 2025.

Builds on

10 of 35 references.

References · 10

  1. 2018Model Cards for Model Reportingcited by 1,080
  2. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 956
  3. 2021Can Machines Learn Morality? The Delphi Experimentcited by 141
  4. 2021What's in the Box? A Preliminary Analysis of Undesirable Content in the Common Crawl Corpuscited by 80
  5. 2019Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?cited by 64
  6. 2022Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selectioncited by 53
  7. 2021Changing the World by Changing the Datacited by 51
  8. 2021The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Failcited by 38
  9. 2021A Word on Machine Ethics: A Response to Jiang et al. (2021)cited by 23
  10. 2021How to Write a Bias Statement: Recommendations for Submissions to the Workshop on Gender Bias in NLPcited by 5

Built on by

Not cited yet.