A Word on Machine Ethics: A Response to Jiang et al. (2021)

• URL: http://arxiv.org/abs/2111.04158v1
• Date: Sun, 7 Nov 2021 19:31:51 GMT
• Title: A Word on Machine Ethics: A Response to Jiang et al. (2021)
• Authors: Zeerak Talat, Hagen Blix, Josef Valvoda, Maya Indira Ganesh, Ryan Cotterell, Adina Williams
• Abstract summary: We focus on a single case study of the recently proposed Delphi model and offer a critique of the project's proposed method of automating morality judgments. We conclude with a discussion of how machine ethics could usefully proceed, by focusing on current and near-future uses of technology.
• Score: 36.955224006838584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ethics is one of the longest standing intellectual endeavors of humanity. In recent years, the fields of AI and NLP have attempted to wrangle with how learning systems that interact with humans should be constrained to behave ethically. One proposal in this vein is the construction of morality models that can take in arbitrary text and output a moral judgment about the situation described. In this work, we focus on a single case study of the recently proposed Delphi model and offer a critique of the project's proposed method of automating morality judgments. Through an audit of Delphi, we examine broader issues that would be applicable to any similar attempt. We conclude with a discussion of how machine ethics could usefully proceed, by focusing on current and near-future uses of technology, in a way that centers around transparency, democratic values, and allows for straightforward accountability.

Related papers

• Towards a Feminist Metaethics of AI [0.0]
  I argue that these insufficiencies could be mitigated by developing a research agenda for a feminist metaethics of AI. Applying this perspective to the context of AI, I suggest that a feminist metaethics of AI would examine: (i) the continuity between theory and action in AI ethics; (ii) the real-life effects of AI ethics; (iii) the role and profile of those involved in AI ethics; and (iv) the effects of AI on power relations through methods that pay attention to context, emotions and narrative.
  arXiv  Detail & Related papers  (2023-11-10T13:26:45Z)
• Values, Ethics, Morals? On the Use of Moral Concepts in NLP Research [13.28561989789828]
  We provide an overview of some important ethical concepts stemming from philosophy. We systematically survey the existing literature on moral NLP. Our findings show that, for instance, most papers neither provide a clear definition of the terms they use nor adhere to definitions from philosophy.
  arXiv  Detail & Related papers  (2023-10-21T06:04:10Z)
• Rethinking Machine Ethics -- Can LLMs Perform Moral Reasoning through the Lens of Moral Theories? [78.3738172874685]
  Making moral judgments is an essential step toward developing ethical AI systems. Prevalent approaches are mostly implemented in a bottom-up manner, which uses a large set of annotated data to train models based on crowd-sourced opinions about morality. This work proposes a flexible top-down framework to steer (Large) Language Models (LMs) to perform moral reasoning with well-established moral theories from interdisciplinary research.
  arXiv  Detail & Related papers  (2023-08-29T15:57:32Z)
• AiSocrates: Towards Answering Ethical Quandary Questions [51.53350252548668]
  AiSocrates is a system for deliberative exchange of different perspectives to an ethical quandary. We show that AiSocrates generates promising answers to ethical quandary questions with multiple perspectives. We argue that AiSocrates is a promising step toward developing an NLP system that incorporates human values explicitly by prompt instructions.
  arXiv  Detail & Related papers  (2022-05-12T09:52:59Z)
• Contextualizing Artificially Intelligent Morality: A Meta-Ethnography of Top-Down, Bottom-Up, and Hybrid Models for Theoretical and Applied Ethics in Artificial Intelligence [0.0]
  In this meta-ethnography, we explore three different angles of ethical artificial intelligence (AI) design implementation. The novel contribution to this framework is the political angle, which constitutes ethics in AI either being determined by corporations and governments and imposed through policies or law (coming from the top) There is a focus on reinforcement learning as an example of a bottom-up applied technical approach and AI ethics principles as a practical top-down approach.
  arXiv  Detail & Related papers  (2022-04-15T18:47:49Z)
• Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
  Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research. An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system. We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
  arXiv  Detail & Related papers  (2022-04-11T14:36:39Z)
• Descriptive AI Ethics: Collecting and Understanding the Public Opinion [10.26464021472619]
  This work proposes a mixed AI ethics model that allows normative and descriptive research to complement each other. We discuss its implications on bridging the gap between optimistic and pessimistic views towards AI systems' deployment.
  arXiv  Detail & Related papers  (2021-01-15T03:46:27Z)
• Case Study: Deontological Ethics in NLP [119.53038547411062]
  We study one ethical theory, namely deontological ethics, from the perspective of NLP. In particular, we focus on the generalization principle and the respect for autonomy through informed consent. We provide four case studies to demonstrate how these principles can be used with NLP systems.
  arXiv  Detail & Related papers  (2020-10-09T16:04:51Z)
• Contextualized moral inference [12.574316678945195]
  We present a text-based approach that predicts people's intuitive judgment of moral vignettes. We show that a contextualized representation offers a substantial advantage over alternative representations.
  arXiv  Detail & Related papers  (2020-08-25T00:34:28Z)
• Aligning AI With Shared Human Values [85.2824609130584]
  We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. We find that current language models have a promising but incomplete ability to predict basic human ethical judgements. Our work shows that progress can be made on machine ethics today, and it provides a steppingstone toward AI that is aligned with human values.
  arXiv  Detail & Related papers  (2020-08-05T17:59:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.