Computational Machine Ethics: A Survey
Authors: Tammy Zhong, Yang Song, Raynaldio Limarga, Maurice Pagnucco
JAIR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This survey provides an overview of CME, highlighting the breadth of directions and the use of techniques within the field. We also provide some background on the ethical dimension before introducing our taxonomy used to categorise and detail the variety of existing approaches from a more technical perspective. Finally, we identify limitations in the research and suggest potential open challenges for future work. |
| Researcher Affiliation | Academia | Tammy Zhong EMAIL Yang Song EMAIL Raynaldio Limarga EMAIL Maurice Pagnucco EMAIL School of Computer Science and Engineering The University of New South Wales, Sydney, NSW 2052, AUSTRALIA. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks for its own survey methodology. It discusses various approaches from other works, some of which may contain pseudocode, but none are presented as part of this paper's direct contribution. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described in this survey. |
| Open Datasets | Yes | Notable datasets include SCRUPLES (Lourie et al., 2021), featuring 32k real-life ethical situations, and SOCIAL-CHEM-101 (Forbes et al., 2020), offering real-life scenarios and Rules of Thumb from subreddits, ROCStories (Mostafazadeh et al., 2016), and Dear Abby. The MORAL STORIES corpus (Emelin et al., 2021) comprises 12k crowd-sourced stories depicting ethical reasoning in daily life. ETHICS (Hendrycks, Burns, et al., 2021) integrates well-established ethical theories, emphasising clarity and removing scenarios with low worker agreement rates. The SOCIAL BIAS INFERENCE CORPUS (Sap et al., 2020) focuses on ethical situations related to social biases from various social media platforms. |
| Dataset Splits | No | This paper is a survey of existing research and does not conduct its own experiments. Therefore, it does not specify any training/test/validation dataset splits. |
| Hardware Specification | No | This paper is a survey and does not describe the hardware used for any experiments, as it does not present new empirical work. |
| Software Dependencies | No | The paper, being a survey, does not specify any particular ancillary software dependencies with version numbers for its own methodology. |
| Experiment Setup | No | This paper is a survey and does not contain details about an experimental setup, hyperparameters, or training configurations, as it does not present new empirical work. |