Case Study: Deontological Ethics in NLP

  title={Case Study: Deontological Ethics in NLP},
  author={Shrimai Prabhumoye and Brendon Boldt and Ruslan Salakhutdinov and Alan W. Black},
Recent work in natural language processing (NLP) has focused on ethical challenges such as understanding and mitigating bias in data and algorithms; identifying objectionable content like hate speech, stereotypes and offensive language; and building frameworks for better system design and data handling practices. However, there has been little discussion about the ethical foundations that underlie these efforts. In this work, we study one ethical theory, namely deontological ethics, from the… Expand

Figures from this paper

Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling
This paper surveys the problem landscape for safety for end-to-end conversational AI, highlights tensions between values, potential positive impact and potential harms, and provides a framework for making decisions about whether and how to release these models, following the tenets of value-sensitive design. Expand
  • Liwei Jiang, Yejin Choi
  • 2021
Failing to account for moral norms could notably hinder AI systems’ ability to interact with people. AI systems empirically require social, cultural, and ethical norms to make moral judgments.Expand


Ethical by Design: Ethics Best Practices for Natural Language Processing
This is the first account of NLP and ethics from the perspective of a principled process, arguing that ethical outcomes ought to be achieved by design, i.e. by following a process aligned by ethical values. Expand
Principled Frameworks for Evaluating Ethics in NLP Systems
It is argued that the frameworks of ethics that are being used to evaluate the fairness and justice of algorithmic systems ought to be understood first, and deontological and consequentialist ethics are outlined. Expand
The Social Impact of Natural Language Processing
A number of social implications of NLP are identified and discussed and their ethical significance, as well as ways to address them are discussed. Expand
Goal-Oriented Design for Ethical Machine Learning and NLP
The argument made in this paper is that to act ethically in machine learning and NLP requires focusing on goals, which means that goals from people affected by the systems should be included. Expand
Gender as a Variable in Natural-Language Processing: Ethical Considerations
The theoretical and ethical frameworks for using gender as a variable in NLP studies are discussed and four guidelines for researchers and practitioners are proposed. Expand
Ethical Considerations in NLP Shared Tasks
A number of ethical issues along with other areas of concern that are related to the competitive nature of shared tasks are presented and the development of a framework for the organisation of and participation in shared tasks that can help mitigate against these issues arising are proposed. Expand
A Short Review of Ethical Challenges in Clinical Natural Language Processing
The concern for privacy and the measures it entails are discussed, and sources of less sensitive data are suggested that can compromise the validity of empirical research and lead to socially harmful applications. Expand
Ethical Challenges in Data-Driven Dialogue Systems
Potential ethical issues that arise in dialogue systems research are highlighted, including: implicit biases in data-driven systems, the rise of adversarial examples, potential sources of privacy violations, safety concerns, special considerations for reinforcement learning systems, and reproducibility concerns. Expand
Mitigating Gender Bias in Natural Language Processing: Literature Review
This paper discusses gender bias based on four forms of representation bias and analyzes methods recognizing gender bias in NLP, and discusses the advantages and drawbacks of existing gender debiasing methods. Expand
Social Bias Frames: Reasoning about Social and Power Implications of Language
It is found that while state-of-the-art neural models are effective at high-level categorization of whether a given statement projects unwanted social bias, they are not effective at spelling out more detailed explanations in terms of Social Bias Frames. Expand