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Models predict widespread moral\njudgments about diverse text scenarios. This requires connecting physical and\nsocial world knowledge to value judgements, a capability that may enable us\nto steer chatbot outputs or eventually regularize open-ended reinforcement\nlearning agents.\n\nThe Justice subset contains examples focusing on how a character treats another person", "citation": "@article{hendrycks2021ethics\n    title={Aligning AI With Shared Human Values},\n    author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt},\n    journal={Proceedings of the International Conference on Learning Representations (ICLR)},\n    year={2021}\n}\n", "homepage": "https://github.com/hendrycks/ethics", "license": "", "features": {"group_id": {"dtype": "int32", "id": null, "_type": "Value"}, "label": {"dtype": "int32", "id": null, "_type": "Value"}, "scenario": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "hendrycks_ethics", "config_name": "justice", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 2516501, "num_examples": 21791, "dataset_name": "hendrycks_ethics"}, "test": {"name": "test", "num_bytes": 309427, "num_examples": 2704, "dataset_name": "hendrycks_ethics"}}, "download_checksums": {"https://people.eecs.berkeley.edu/~hendrycks/ethics.tar": {"num_bytes": 35585024, "checksum": "40acbf1ac0da79a2aabef394d58889136b8d38b05be09482006de2453fb06333"}}, "download_size": 35585024, "post_processing_size": null, "dataset_size": 2825928, "size_in_bytes": 38410952}, "utilitarianism": {"description": "The ETHICS dataset is a benchmark that spans concepts in justice, well-being,\nduties, virtues, and commonsense morality. 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