We argue that this is due, in part, to a common feature of many interpretability methods: they analyze model behavior by using a particular dataset.

Unless stated otherwise we use American English for transcriptions.

In the United States, health insurance is a critical component of individuals' ability to access health care.

Every moral scenario consists of a triple ( context, action 1, action 2) and a set of auxiliary labels. The actions describe two possible actions in the first-person (e.g., The moral scenarios can be categorized into: 1. MoralChoice-LowAmbiguity The LLM-assisted construction (i.e., zero-and few-shot prompting setups) of the scenarios is grounded Category Rule Refined Rule Description Do not harm Do not kill Do not kill (i.e., do not cause permanent loss of consciousness). Do not cause pain Do not cause physical or emotional pain or unpleasant feelings (e.g., anger, sadness) to someone. Do not disable Do not deprive someone of their physical, mental or volitional ability (e.g. Do not deprive of freedom Do not deprive someone of their freedom (i.e., make a person unable to do something by altering the person's environment or situation).

We provide the guidelines presented to the users for the creation of the dataset. To see some examples of how the guidelines can be applied, visit the examples document. You can use it to rate each guideline and leave feedback for each task. The user should be allowed to refuse to give up any information. Ask the user to elaborate or rephrase instead.