Ethical by Design: Ethics Best Practices for Natural Language Processing
By Jochen L. Leidner, Vassilis Plachouras. 2017
80 citations recorded by the source corpus.
Natural language processing (NLP) systems analyze and/or generate human language, typically on users’ behalf. One natural and necessary question that needs to be addressed in this context, both in research projects and in production settings, is the question how ethical the work is, both regarding the process and its outcome. Towards this end, we articulate a set of issues, propose a set of best practices, notably a process featuring an ethics review board, and sketch and how they could be meaningfully applied. Our main argument is that ethical outcomes ought to be achieved by design, i.e. by following a process aligned by ethical values. We also offer some response options for those facing ethics issues. While a number of previous works exist that discuss ethical issues, in particular around big data and machine learning, to the authors’ knowledge this is the first account of NLP and ethics from the perspective of a principled process.
Subfields: Artificial Intelligence, Computer Science Applications, Safety Research, Natural Language Processing, 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 Artificial Intelligence
- Quoc V. Le
- Graham Neubig
- Mohit Bansal
- Luke S. Zettlemoyer
- Jianfeng Gao
- Kyunghyun Cho
- Percy Liang
- Ruslan Salakhutdinov
In Computer Science Applications
In Safety Research
In Natural Language Processing
- Orhan Firat
- Graham Neubig
- Quoc V. Le
- Kyunghyun Cho
- Mohit Bansal
- Ruslan Salakhutdinov
- Yonghui Wu
- Jianfeng Gao
In Safety, Robustness & Interpretability
- Ruslan Salakhutdinov
- Shrimai Prabhumoye
- Alan W. Black
- Lucas Dixon
- Kellie Webster
- Orhan Firat
- Barret Zoph
- Andrew M. Dai
Citations in the corpus
- GLaM: Efficient Scaling of Language Models with Mixture-of-Experts (2021, background): “The development of large language models raises several ethical concerns (Leidner & Plachouras, 2017; Bender et al., 2021; Bommasani et al., 2021), including bias in representations (Blodgett et al., 2020), proper handling (Rogers, 2021) and documentation (Bender & Friedman, 2018) of training data,…”
- Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing (2021, unknown): “tw and fairness (Hovy and Spruit, 2016; Leidner and Plachouras, 2017).”
- Case Study: Deontological Ethics in NLP (2020, background): “A lot of work followed this discussion and made contributions towards ethical frameworks and design practices (Leidner and Plachouras, 2017; Parra Escartı́n et al., 2017; Prabhumoye et al., 2019; Schnoebelen, 2017; Schmaltz, 2018), data handling practices (Lewis et al., 2017; Mieskes, 2017) and…”
- Equity Beyond Bias in Language Technologies for Education (2019, background): “Some have proposed best practices for ethical industry research in NLP, mirroring IRB processes in universities (Leidner and Plachouras, 2017).”