Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis?
By Kobi Leins, Jey Han Lau, Timothy Baldwin. 2020
44 citations recorded by the source corpus.
As part of growing NLP capabilities, coupled with an awareness of the ethical dimensions of research, questions have been raised about whether particular datasets and tasks should be deemed off-limits for NLP research. We examine this question with respect to a paper on automatic legal sentencing from EMNLP 2019 which was a source of some debate, in asking whether the paper should have been allowed to be published, who should have been charged with making such a decision, and on what basis. We focus in particular on the role of data statements in ethically assessing research, but also discuss the topic of dual use, and examine the outcomes of similar debates in other scientific disciplines.
Subfields: Safety, Robustness & Interpretability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Safety, Robustness & Interpretability
- Yulia Tsvetkov
- Anjalie Field
- Su Lin Blodgett
- Zeerak Talat
- Samuel R. Bowman
- Ethan Perez
- Thomas Liao
- Saurav Kadavath
Citations in the corpus
- The Capacity for Moral Self-Correction in Large Language Models (2023, background): “Scientifically, this may be useful as an additional experimental condition to test for misuse, as in [64], but practically there is much debate surrounding how to appropriately study dual-use issues arising from language models [22, 31].”
- A Survey of Race, Racism, and Anti-Racism in NLP (2021, methodology): “In the context of perpetuating racism, examples include criticism of tools for predicting demographic information (Tatman, 2020) and automatic prison term prediction (Leins et al., 2020), motivated by the history of using technology to police racial minorities and related criticism in other fields (”