Goal-Oriented Design for Ethical Machine Learning and NLP
By Tyler Schnoebelen. 2017
7 citations recorded by the source corpus.
The argument made in this paper is that to act ethically in machine learning and NLP requires focusing on goals. NLP projects are often classificatory systems that deal with human subjects, which means that goals from people affected by the systems should be included. The paper takes as its core example a model that detects criminality, showing the problems of training data, categories, and outcomes. The paper is oriented to the kinds of critiques on power and the reproduction of inequality that are found in social theory, but it also includes concrete suggestions on how to put goal-oriented design into practice.
Subfields: Artificial Intelligence, Safety Research, Sociology and Political Science, 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
- Ruslan Salakhutdinov
- Yejin Choi
- Hal Daumé Iii
- Percy Liang
- Noah A. Smith
- Mohit Bansal
- Yoav Goldberg
- Yoshua Bengio
In Safety Research
- Hanna M. Wallach
- Su Lin Blodgett
- Alan W. Black
- Ruslan Salakhutdinov
- Hal Daumé Iii
- Solon Barocas
- Adrian Weller
- Alexandra Chouldechova
In Sociology and Political Science
- Su Lin Blodgett
- Ruslan Salakhutdinov
- Alan W. Black
- Hal Daumé Iii
- Hanna M. Wallach
- Solon Barocas
- Shrimai Prabhumoye
In Natural Language Processing
- Ruslan Salakhutdinov
- Hal Daumé Iii
- Shrimai Prabhumoye
- Su Lin Blodgett
- Yejin Choi
- Alan W. Black
- Hanna M. Wallach
- Solon Barocas
In Safety, Robustness & Interpretability
- Su Lin Blodgett
- Hal Daumé Iii
- Ruslan Salakhutdinov
- Hanna M. Wallach
- Shrimai Prabhumoye
- Alan W. Black
- Solon Barocas
- Emilio Ferrara
Citations in the corpus
- Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “Surveys, frameworks, and meta-analyses Hovy and Spruit (2016); Larson (2017); McCurdy and Serbetçi (2017); Schnoebelen (2017); Basta et al. (2019); Ethayarajh et al. (2019); Gonen and Goldberg (2019); Lauscher and Glavaš (2019); Loukina et al. (2019); Mayfield et al. (2019); Mirzaev et al. (2019);…”
- Case Study: Deontological Ethics in NLP (2020, background): “…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 specific domains like education (Mayfield et…”