On the Machine Learning of Ethical Judgments from Natural Language

Zeerak Talat, Hagen Blix, Josef Valvoda, Maya Indira Ganesh, Ryan Cotterell, Adina Williams


Abstract
Ethics is one of the longest standing intellectual endeavors of humanity. In recent years, the fields of AI and NLP have attempted to address issues of harmful outcomes in machine learning systems that are made to interface with humans. One recent approach in this vein is the construction of NLP morality models that can take in arbitrary text and output a moral judgment about the situation described. In this work, we offer a critique of such NLP methods for automating ethical decision-making. Through an audit of recent work on computational approaches for predicting morality, we examine the broader issues that arise from such efforts. We conclude with a discussion of how machine ethics could usefully proceed in NLP, by focusing on current and near-future uses of technology, in a way that centers around transparency, democratic values, and allows for straightforward accountability.
Anthology ID:
2022.naacl-main.56
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
769–779
Language:
URL:
https://aclanthology.org/2022.naacl-main.56
DOI:
10.18653/v1/2022.naacl-main.56
Bibkey:
Cite (ACL):
Zeerak Talat, Hagen Blix, Josef Valvoda, Maya Indira Ganesh, Ryan Cotterell, and Adina Williams. 2022. On the Machine Learning of Ethical Judgments from Natural Language. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 769–779, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
On the Machine Learning of Ethical Judgments from Natural Language (Talat et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.56.pdf
Data
ETHICS