Summary of Some Issues in Predictive Ethics Modeling: An Annotated Contrast Set Of “moral Stories”, by Ben Fitzgerald

Some Issues in Predictive Ethics Modeling: An Annotated Contrast Set of “Moral Stories”

by Ben Fitzgerald

First submitted to arxiv on: 7 Jul 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The paper challenges the accuracy-based approach in ethics modeling, specifically addressing the issue of translating moral dilemmas into text-based input. It highlights the limitations of popular models like Delphi, which excel at labeling ethical dilemmas but may not accurately grasp moral nuances. The study demonstrates these limitations using contrast sets that significantly reduce the performance of classifiers trained on the Moral Stories dataset. By analyzing specific forms of data misrepresentation, the authors provide concrete estimates of how these issues impact classifier accuracy.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This research shows that ethics models like Delphi aren’t perfect. They’re great at saying whether something is right or wrong, but they might not understand the reasons behind those judgments. The study found that even small changes in wording can greatly reduce the model’s accuracy. For example, adding just a few words to describe a situation can make it much harder for the model to correctly identify what’s moral or immoral.