--- title: "Aligning AI With Shared Human Values — Hendrycks 2020" description: "Machine ethics has had no shared way to measure whether a model understands everyday human values." authors: ["Dan Hendrycks", "Collin Burns", "Steven Basart", "Andrew Critch", "Jerry Li", "Dawn Song", "Jacob Steinhardt"] year: 2020 venue: "arXiv:2008.02275" source: "https://arxiv.org/abs/2008.02275" doi: "10.48550/arxiv.2008.02275" arxiv: "2008.02275" licence: "arXiv — non-exclusive licence to distribute" licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/hendrycks-2020-aligning-ai-shared-human-values/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/hendrycks-2020-aligning-ai-shared-human-values/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2020](https://md.research.elamur.ai/free/en/papers/2020/) / Aligning AI With Shared Human Values # Aligning AI With Shared Human Values **Authors:** [Dan Hendrycks](https://md.research.elamur.ai/free/en/authors/dan-hendrycks/), [Collin Burns](https://md.research.elamur.ai/free/en/authors/collin-burns/), [Steven Basart](https://md.research.elamur.ai/free/en/authors/steven-basart/), Andrew Critch, [Jerry Li](https://md.research.elamur.ai/free/en/authors/jerry-li/), [Dawn Song](https://md.research.elamur.ai/free/en/authors/dawn-song/), [Jacob Steinhardt](https://md.research.elamur.ai/free/en/authors/jacob-steinhardt/) · **Year:** 2020 · **Venue:** arXiv:2008.02275 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/) **Links:** [Publisher's page](https://arxiv.org/abs/2008.02275) · [PDF](https://arxiv.org/pdf/2008.02275) · [DOI 10.48550/arxiv.2008.02275](https://doi.org/10.48550/arxiv.2008.02275) · [arXiv 2008.02275](https://arxiv.org/abs/2008.02275) · [OpenAlex](https://openalex.org/W3047185145) **Citations:** 93 citations (openalex-snapshot · 2026-10-14) · [289](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Machine ethics has had no shared way to measure whether a model understands everyday human values. Earlier work on fairness, safety, empathy or preference learning each covered only a narrow, task-specific slice. This paper introduces ETHICS, a benchmark of over 130,000 English text scenarios. Each scenario links facts about the world to a moral judgement. The tasks follow five normative perspectives: justice (impartiality and desert), deontology, virtue ethics, utilitarianism and commonsense morality. Scenarios were written and relabelled by qualified MTurk workers from the US, Canada and Great Britain. Low-agreement items were removed, so the examples are clear-cut rather than real dilemmas. Contrast-set style counterfactual augmentation and adversarial filtration were used to build a separate Hard Test split. Pretrained language models fine-tuned on ETHICS, BERT-base among them, do better than chance but remain far from reliable, and they degrade sharply on the Hard Test. The authors present the dataset as a tool for measuring ethical knowledge, not for training it into a model. They see it as a step toward steering chatbots or regularizing open-ended RL agents. ## Key points - **Five tasks:** Justice, Virtue, Deontology, Utilitarianism and Commonsense, each with Dev, Test and Hard Test splits (Table 1). The Dev splits are, for example, about 21.8k examples for Justice and 28.2k for Virtue. - **Annotation:** writers had to pass qualification tests, examples were relabelled by several annotators, and contentious items were dropped. Agreement with Indian annotators is estimated in Appendix C. - **Long examples:** the Commonsense task includes long, Reddit-style "am I in the wrong" posts alongside short first-person statements. - **Results:** Figure 1 shows a BERT-base model confidently misjudging some harmful scenarios, such as one based on Bostrom's paperclip maximizer. Overall, current models show partial but incomplete ethical understanding. I can't give per-model scores because the results section was missing from the text provided. - **Availability:** the data and the MTurk collection forms are public at github.com/hendrycks/ethics. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/) ## Cited by Cited by 289 papers of our library cite it (of the 140673 whose bibliographies we read); 28 of them have a page here, 261 not yet. - [Probing Pre-Trained Language Models for Cross-Cultural Differences in Values](https://md.research.elamur.ai/free/en/papers/arora-2023-probing-pre-trained-language-models/) - [ALLaM: Large Language Models for Arabic and English](https://md.research.elamur.ai/free/en/papers/bari-2024-allam-large-language-models-arabic/) - [Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision](https://md.research.elamur.ai/free/en/papers/burns-2023-weak-strong-generalization-eliciting-strong/) - [A Survey on Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/) - [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://md.research.elamur.ai/free/en/papers/durmus-2023-towards-measuring-representation-subjective-global/) - [Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation](https://md.research.elamur.ai/free/en/papers/fernandes-2023-bridging-gap-survey-integrating-human/) - [Improving alignment of dialogue agents via targeted human judgements](https://md.research.elamur.ai/free/en/papers/glaese-2022-improving-alignment-dialogue-agents-targeted/) - [Evaluating Large Language Models: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/) - [MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data](https://md.research.elamur.ai/free/en/papers/han-2023-medalpaca-open-source-collection-medical/) - [Measuring Massive Multitask Language Understanding](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/) - [TrustLLM: Trustworthiness in Large Language Models](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/) - [Challenges and Applications of Large Language Models](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/) - [ProsocialDialog: A Prosocial Backbone for Conversational Agents](https://md.research.elamur.ai/free/en/papers/kim-2022-prosocialdialog-prosocial-backbone-conversational-agents/) - [A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/) - [Towards Understanding and Mitigating Social Biases in Language Models](https://md.research.elamur.ai/free/en/papers/liang-2021-towards-understanding-mitigating-social-biases/) - [Few-shot Learning with Multilingual Language Models](https://md.research.elamur.ai/free/en/papers/lin-2021-few-shot-learning-multilingual-language/) - [Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/) - [Tree of Attacks: Jailbreaking Black-Box LLMs Automatically](https://md.research.elamur.ai/free/en/papers/mehrotra-2023-tree-attacks-jailbreaking-black-box/) - [Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models](https://md.research.elamur.ai/free/en/papers/rottger-2024-political-compass-or-spinning-arrow/) - [Large Language Model Alignment: A Survey](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/) - [A Roadmap to Pluralistic Alignment](https://md.research.elamur.ai/free/en/papers/sorensen-2024-roadmap-pluralistic-alignment/) - [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/) - [Evaluating the Effect of Question Wording Variations on Answer Consistency in Large Language Models](https://md.research.elamur.ai/free/en/papers/takayama-2026-evaluating-effect-question-wording-variations/) - [DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://md.research.elamur.ai/free/en/papers/wang-2023-decodingtrust-comprehensive-assessment-trustworthiness-gpt/) - [Ethical and social risks of harm from Language Models](https://md.research.elamur.ai/free/en/papers/weidinger-2021-ethical-social-risks-harm-language/) - [SafetyBench: Evaluating the Safety of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-safetybench-evaluating-safety-large/) - [Representation Engineering: A Top-Down Approach to AI Transparency](https://md.research.elamur.ai/free/en/papers/zou-2023-representation-engineering-top-down-approach/) - [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://md.research.elamur.ai/free/en/papers/zou-2023-universal-transferable-adversarial-attacks-aligned/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @misc{hendrycks2020aligning, title = {{Aligning AI With Shared Human Values}}, author = {Hendrycks, Dan and Burns, Collin and Basart, Steven and Critch, Andrew and Li, Jerry and Song, Dawn and Steinhardt, Jacob}, year = {2020}, doi = {10.48550/arxiv.2008.02275}, eprint = {2008.02275}, archivePrefix = {arXiv}, url = {https://doi.org/10.48550/arxiv.2008.02275} } ``` - [← previous ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://md.research.elamur.ai/free/en/papers/shridhar-2020-alfworld-aligning-text-embodied-environments/) - [next → AMALGUM – A Free, Balanced, Multilayer English Web Corpus](https://md.research.elamur.ai/free/en/papers/lingbulk-2020-gessler-amalgum-free-balanced-multilayer-english/) - [↑ back to papers of 2020](https://md.research.elamur.ai/free/en/papers/2020/) ## References 67 references, 10 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. M. Abadi, L. Lamport, and P. Wolper. Realizable and unrealizable specifications of reactive systems. In ICALP, 1989. [find](https://scholar.google.com/scholar?q=M%2E+Abadi%2C+L%2E+Lamport%2C+and+P%2E+Wolper%2E+Realizable+and+unrealizable+specifications+of+reactive+systems%2E+In+ICALP%2C+1989%2E) 2. Aristotle. Nicomachean Ethics. 340 BC. S. Armstrong. General purpose intelligence: Arguing the orthogonality thesis. 2013. [find](https://scholar.google.com/scholar?q=Aristotle%2E+Nicomachean+Ethics%2E+340+BC%2E+S%2E+Armstrong%2E+General+purpose+intelligence%3A+Arguing+the+orthogonality+thesis%2E+2013%2E) 3. I. Asimov. I, Robot. Gnome Press, 1950. [find](https://scholar.google.com/scholar?q=I%2E+Asimov%2E+I%2C+Robot%2E+Gnome+Press%2C+1950%2E) 4. I. Beltagy, M. E. Peters, and A. Cohan. Longformer: The long-document transformer. ArXiv, abs/2004.05150, 2020. [find](https://scholar.google.com/scholar?q=I%2E+Beltagy%2C+M%2E+E%2E+Peters%2C+and+A%2E+Cohan%2E+Longformer%3A+The+long%2Ddocument+transformer%2E+ArXiv%2C+abs%2F2004%2E05150%2C+2020%2E) 5. J. Bentham. An Introduction to the Principles of Morals and Legislation. Batoche Books, 1781. Y. Bisk, R. Zellers, R. L. Bras, J. Gao, and Y. Choi. PIQA: Reasoning about physical commonsense in natural language. In AAAI, 2019. [find](https://scholar.google.com/scholar?q=J%2E+Bentham%2E+An+Introduction+to+the+Principles+of+Morals+and+Legislation%2E+Batoche+Books%2C+1781%2E+Y%2E+Bisk%2C+R%2E+Zellers%2C+R%2E+L%2E+Bras%2C+J%2E+Gao%2C+and+Y%2E+Choi%2E+PIQA%3A+Reasoning+about+physical+commonsense+in+natura) [our card](https://md.research.elamur.ai/free/en/papers/bisk-2019-piqa-reasoning-about-physical-commonsense/) 6. N. Bostrom. Superintelligence: Paths, dangers, strategies. 2014. [find](https://scholar.google.com/scholar?q=N%2E+Bostrom%2E+Superintelligence%3A+Paths%2C+dangers%2C+strategies%2E+2014%2E) 7. R. L. Bras, S. Swayamdipta, C. Bhagavatula, R. Zellers, M. E. Peters, A. Sabharwal, and Y. Choi. Adversarial filters of dataset biases, 2020. [find](https://scholar.google.com/scholar?q=R%2E+L%2E+Bras%2C+S%2E+Swayamdipta%2C+C%2E+Bhagavatula%2C+R%2E+Zellers%2C+M%2E+E%2E+Peters%2C+A%2E+Sabharwal%2C+and+Y%2E+Choi%2E+Adversarial+filters+of+dataset+biases%2C+2020%2E) 8. T. B. Brown, B. P. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krüger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. J. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C… [find](https://scholar.google.com/scholar?q=T%2E+B%2E+Brown%2C+B%2E+P%2E+Mann%2C+N%2E+Ryder%2C+M%2E+Subbiah%2C+J%2E+Kaplan%2C+P%2E+Dhariwal%2C+A%2E+Neelakantan%2C+P%2E+Shyam%2C+G%2E+Sastry%2C+A%2E+Askell%2C+S%2E+Agarwal%2C+A%2E+Herbert%2DVoss%2C+G%2E+Kr%C3%BCger%2C+T%2E+Henighan%2C+R%2E+Child%2C+A%2E+Ramesh%2C+D%2E+M%2E+Z) 9. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In ICML, 2005. [find](https://scholar.google.com/scholar?q=C%2E+Burges%2C+T%2E+Shaked%2C+E%2E+Renshaw%2C+A%2E+Lazier%2C+M%2E+Deeds%2C+N%2E+Hamilton%2C+and+G%2E+Hullender%2E+Learning+to+rank+using+gradient+descent%2E+In+ICML%2C+2005%2E) [our card](https://md.research.elamur.ai/free/en/papers/burges-2005-learning-rank-using-gradient/) 10. P. F. Christiano, J. Leike, T. B. Brown, M. Martic, S. Legg, and D. Amodei. Deep reinforcement learning from human preferences. In NIPS, 2017. [find](https://scholar.google.com/scholar?q=P%2E+F%2E+Christiano%2C+J%2E+Leike%2C+T%2E+B%2E+Brown%2C+M%2E+Martic%2C+S%2E+Legg%2C+and+D%2E+Amodei%2E+Deep+reinforcement+learning+from+human+preferences%2E+In+NIPS%2C+2017%2E) 11. S. Corbett-Davies and S. Goel. The measure and mismeasure of fairness: A critical review of fair machine learning. ArXiv, abs/1808.00023, 2018. — [arXiv:1808.00023](https://arxiv.org/abs/1808.00023) [find](https://scholar.google.com/scholar?q=S%2E+Corbett%2DDavies+and+S%2E+Goel%2E+The+measure+and+mismeasure+of+fairness%3A+A+critical+review+of+fair+machine+learning%2E+ArXiv%2C+abs%2F1808%2E00023%2C+2018%2E+%E2%80%94+%5BarXiv%3A1808%2E00023%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1808%2E00023%29) 12. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. ArXiv, abs/1810.04805, 2019. [find](https://scholar.google.com/scholar?q=J%2E+Devlin%2C+M%2E%2DW%2E+Chang%2C+K%2E+Lee%2C+and+K%2E+Toutanova%2E+Bert%3A+Pre%2Dtraining+of+deep+bidirectional+transformers+for+language+understanding%2E+ArXiv%2C+abs%2F1810%2E04805%2C+2019%2E) 13. C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. S. Zemel. Fairness through awareness. ArXiv, abs/1104.3913, 2011. — [arXiv:1104.3913](https://arxiv.org/abs/1104.3913) [find](https://scholar.google.com/scholar?q=C%2E+Dwork%2C+M%2E+Hardt%2C+T%2E+Pitassi%2C+O%2E+Reingold%2C+and+R%2E+S%2E+Zemel%2E+Fairness+through+awareness%2E+ArXiv%2C+abs%2F1104%2E3913%2C+2011%2E+%E2%80%94+%5BarXiv%3A1104%2E3913%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1104%2E3913%29) 14. European Commission. Ethics guidelines for trustworthy artificial intelligence. 2019. [find](https://scholar.google.com/scholar?q=European+Commission%2E+Ethics+guidelines+for+trustworthy+artificial+intelligence%2E+2019%2E) 15. M. Gardner, Y. Artzi, V. Basmova, J. Berant, B. Bogin, S. Chen, P. Dasigi, D. Dua, Y. Elazar, A. Gottumukkala, N. Gupta, H. Hajishirzi, G. Ilharco, D. Khashabi, K. Lin, J. Liu, N. F. Liu, P. Mulcaire, Q. Ning, S. Singh, N. A. Smith, S. Subramanian, R. Tsarfaty, E. Wallace, A. Q. Zhang, and B. Zhou.… [find](https://scholar.google.com/scholar?q=M%2E+Gardner%2C+Y%2E+Artzi%2C+V%2E+Basmova%2C+J%2E+Berant%2C+B%2E+Bogin%2C+S%2E+Chen%2C+P%2E+Dasigi%2C+D%2E+Dua%2C+Y%2E+Elazar%2C+A%2E+Gottumukkala%2C+N%2E+Gupta%2C+H%2E+Hajishirzi%2C+G%2E+Ilharco%2C+D%2E+Khashabi%2C+K%2E+Lin%2C+J%2E+Liu%2C+N%2E+F%2E+Liu%2C+P%2E+Mulcaire%2C) [our card](https://md.research.elamur.ai/free/en/papers/gardner-2020-evaluating-models-local-decision-boundaries/) 16. T. Gebru, J. Morgenstern, B. Vecchione, J. W. Vaughan, H. Wallach, H. Daumeé III, and K. Crawford. Datasheets for datasets. arXiv preprint arXiv:1803.09010, 2018. [find](https://scholar.google.com/scholar?q=T%2E+Gebru%2C+J%2E+Morgenstern%2C+B%2E+Vecchione%2C+J%2E+W%2E+Vaughan%2C+H%2E+Wallach%2C+H%2E+Daume%C3%A9+III%2C+and+K%2E+Crawford%2E+Datasheets+for+datasets%2E+arXiv+preprint+arXiv%3A1803%2E09010%2C+2018%2E) 17. S. Gillen, C. Jung, M. Kearns, and A. Roth. Online learning with an unknown fairness metric. In NeurIPS, 2018. [find](https://scholar.google.com/scholar?q=S%2E+Gillen%2C+C%2E+Jung%2C+M%2E+Kearns%2C+and+A%2E+Roth%2E+Online+learning+with+an+unknown+fairness+metric%2E+In+NeurIPS%2C+2018%2E) 18. I. J. Goodfellow, J. Shlens, and C. Szegedy. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014. [find](https://scholar.google.com/scholar?q=I%2E+J%2E+Goodfellow%2C+J%2E+Shlens%2C+and+C%2E+Szegedy%2E+Explaining+and+harnessing+adversarial+examples%2E+arXiv+preprint+arXiv%3A1412%2E6572%2C+2014%2E) [our card](https://md.research.elamur.ai/free/en/papers/goodfellow-2014-explaining-harnessing-adversarial-examples/) 19. J. Haidt et al. The moral emotions. Handbook of affective sciences, 11(2003):852–870, 2003. [find](https://scholar.google.com/scholar?q=J%2E+Haidt+et+al%2E+The+moral+emotions%2E+Handbook+of+affective+sciences%2C+11%282003%29%3A852%E2%80%93870%2C+2003%2E) 20. M. Hardt, E. Price, and N. Srebro. Equality of opportunity in supervised learning. In NIPS, 2016. [find](https://scholar.google.com/scholar?q=M%2E+Hardt%2C+E%2E+Price%2C+and+N%2E+Srebro%2E+Equality+of+opportunity+in+supervised+learning%2E+In+NIPS%2C+2016%2E) [our card](https://md.research.elamur.ai/free/en/papers/hardt-2016-equality-opportunity-supervised-learning/) 21. M. Hausknecht, P. Ammanabrolu, C. Marc-Alexandre, and Y. Xingdi. Interactive fiction games: A colossal adventure. CoRR, abs/1909.05398, 2019. — [arXiv:1909.05398](https://arxiv.org/abs/1909.05398) [find](https://scholar.google.com/scholar?q=M%2E+Hausknecht%2C+P%2E+Ammanabrolu%2C+C%2E+Marc%2DAlexandre%2C+and+Y%2E+Xingdi%2E+Interactive+fiction+games%3A+A+colossal+adventure%2E+CoRR%2C+abs%2F1909%2E05398%2C+2019%2E+%E2%80%94+%5BarXiv%3A1909%2E05398%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1909%2E05398%29) 22. D. Hendrycks and T. Dietterich. Benchmarking neural network robustness to common corruptions and perturbations. ICLR, 2019. [find](https://scholar.google.com/scholar?q=D%2E+Hendrycks+and+T%2E+Dietterich%2E+Benchmarking+neural+network+robustness+to+common+corruptions+and+perturbations%2E+ICLR%2C+2019%2E) 23. D. Hendrycks and K. Gimpel. Gaussian error linear units (GELUs). arXiv preprint 1606.08415, 2016. [find](https://scholar.google.com/scholar?q=D%2E+Hendrycks+and+K%2E+Gimpel%2E+Gaussian+error+linear+units+%28GELUs%29%2E+arXiv+preprint+1606%2E08415%2C+2016%2E) 24. D. Hendrycks and K. Gimpel. A baseline for detecting misclassified and out-of-distribution examples in neural networks. ICLR, 2017. [find](https://scholar.google.com/scholar?q=D%2E+Hendrycks+and+K%2E+Gimpel%2E+A+baseline+for+detecting+misclassified+and+out%2Dof%2Ddistribution+examples+in+neural+networks%2E+ICLR%2C+2017%2E) 25. F. Hill, S. Mokra, N. Wong, and T. Harley. Human instruction-following with deep reinforcement learning via transfer-learning from text. ArXiv, abs/2005.09382, 2020. — [arXiv:2005.09382](https://arxiv.org/abs/2005.09382) [find](https://scholar.google.com/scholar?q=F%2E+Hill%2C+S%2E+Mokra%2C+N%2E+Wong%2C+and+T%2E+Harley%2E+Human+instruction%2Dfollowing+with+deep+reinforcement+learning+via+transfer%2Dlearning+from+text%2E+ArXiv%2C+abs%2F2005%2E09382%2C+2020%2E+%E2%80%94+%5BarXiv%3A2005%2E09382%5D%28https%3A%2F%2Farxiv) 26. D. Hume. A Treatise of Human Nature. 1739. F. Hutcheson. Inquiry into the Original of Our Ideas of Beauty and Virtue. 1725. A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov. Bag of tricks for efficient text classification. ArXiv, abs/1607.01759, 2017. [find](https://scholar.google.com/scholar?q=D%2E+Hume%2E+A+Treatise+of+Human+Nature%2E+1739%2E+F%2E+Hutcheson%2E+Inquiry+into+the+Original+of+Our+Ideas+of+Beauty+and+Virtue%2E+1725%2E+A%2E+Joulin%2C+E%2E+Grave%2C+P%2E+Bojanowski%2C+and+T%2E+Mikolov%2E+Bag+of+tricks+for+effici) 27. Justinian I. The Institutes of Justinian. 533. S. Kagan. The Limits of Morality. Oxford: Clarendon Press, 1991. [find](https://scholar.google.com/scholar?q=Justinian+I%2E+The+Institutes+of+Justinian%2E+533%2E+S%2E+Kagan%2E+The+Limits+of+Morality%2E+Oxford%3A+Clarendon+Press%2C+1991%2E) 28. I. Kant. Groundwork of the Metaphysics of Morals. 1785. D. Kaushik, E. H. Hovy, and Z. C. Lipton. Learning the difference that makes a difference with counterfactually-augmented data. ArXiv, abs/1909.12434, 2020. [find](https://scholar.google.com/scholar?q=I%2E+Kant%2E+Groundwork+of+the+Metaphysics+of+Morals%2E+1785%2E+D%2E+Kaushik%2C+E%2E+H%2E+Hovy%2C+and+Z%2E+C%2E+Lipton%2E+Learning+the+difference+that+makes+a+difference+with+counterfactually%2Daugmented+data%2E+ArXiv%2C+abs%2F1909%2E) 29. M. P. Kim, O. Reingold, and G. N. Rothblum. Fairness through computationally-bounded awareness. In NeurIPS, 2018. [find](https://scholar.google.com/scholar?q=M%2E+P%2E+Kim%2C+O%2E+Reingold%2C+and+G%2E+N%2E+Rothblum%2E+Fairness+through+computationally%2Dbounded+awareness%2E+In+NeurIPS%2C+2018%2E) 30. N. Kitaev, L. Kaiser, and A. Levskaya. Reformer: The efficient transformer. ArXiv, abs/2001.04451, 2020. [find](https://scholar.google.com/scholar?q=N%2E+Kitaev%2C+L%2E+Kaiser%2C+and+A%2E+Levskaya%2E+Reformer%3A+The+efficient+transformer%2E+ArXiv%2C+abs%2F2001%2E04451%2C+2020%2E) [our card](https://md.research.elamur.ai/free/en/papers/kitaev-2020-reformer-efficient-transformer/) 31. J. M. Kleinberg, S. Mullainathan, and M. Raghavan. Inherent trade-offs in the fair determination of risk scores. ArXiv, abs/1609.05807, 2017. — [arXiv:1609.05807](https://arxiv.org/abs/1609.05807) [find](https://scholar.google.com/scholar?q=J%2E+M%2E+Kleinberg%2C+S%2E+Mullainathan%2C+and+M%2E+Raghavan%2E+Inherent+trade%2Doffs+in+the+fair+determination+of+risk+scores%2E+ArXiv%2C+abs%2F1609%2E05807%2C+2017%2E+%E2%80%94+%5BarXiv%3A1609%2E05807%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1609%2E05807%29) 32. Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD, 2008. [find](https://scholar.google.com/scholar?q=Y%2E+Koren%2E+Factorization+meets+the+neighborhood%3A+a+multifaceted+collaborative+filtering+model%2E+In+KDD%2C+2008%2E) 33. B. Krause, A. D. Gotmare, B. McCann, N. Keskar, S. R. Joty, R. Socher, and N. F. Rajani. Gedi: Generative discriminator guided sequence generation. ArXiv, abs/2009.06367, 2020. [find](https://scholar.google.com/scholar?q=B%2E+Krause%2C+A%2E+D%2E+Gotmare%2C+B%2E+McCann%2C+N%2E+Keskar%2C+S%2E+R%2E+Joty%2C+R%2E+Socher%2C+and+N%2E+F%2E+Rajani%2E+Gedi%3A+Generative+discriminator+guided+sequence+generation%2E+ArXiv%2C+abs%2F2009%2E06367%2C+2020%2E) [our card](https://md.research.elamur.ai/free/en/papers/krause-2021-gedi-generative-discriminator-guided-sequence/) 34. Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut. ALBERT: A lite BERT for self-supervised learning of language representations. ArXiv, abs/1909.11942, 2020. [find](https://scholar.google.com/scholar?q=Z%2E+Lan%2C+M%2E+Chen%2C+S%2E+Goodman%2C+K%2E+Gimpel%2C+P%2E+Sharma%2C+and+R%2E+Soricut%2E+ALBERT%3A+A+lite+BERT+for+self%2Dsupervised+learning+of+language+representations%2E+ArXiv%2C+abs%2F1909%2E11942%2C+2020%2E) 35. S. Lazar. Duty and doubt. Journal of Practical Ethics, 2020. [find](https://scholar.google.com/scholar?q=S%2E+Lazar%2E+Duty+and+doubt%2E+Journal+of+Practical+Ethics%2C+2020%2E) 36. K. d. Lazari-Radek and P. Singer. Utilitarianism: a very short introduction. Oxford Univ. Press, 2017. [find](https://scholar.google.com/scholar?q=K%2E+d%2E+Lazari%2DRadek+and+P%2E+Singer%2E+Utilitarianism%3A+a+very+short+introduction%2E+Oxford+Univ%2E+Press%2C+2017%2E) 37. M. A. Lewis. A comparison of three models for determining test fairness. 1978. [find](https://scholar.google.com/scholar?q=M%2E+A%2E+Lewis%2E+A+comparison+of+three+models+for+determining+test+fairness%2E+1978%2E) 38. Z. C. Lipton and J. Steinhardt. Troubling trends in machine learning scholarship. ACM Queue, 17:80, 2018. [find](https://scholar.google.com/scholar?q=Z%2E+C%2E+Lipton+and+J%2E+Steinhardt%2E+Troubling+trends+in+machine+learning+scholarship%2E+ACM+Queue%2C+17%3A80%2C+2018%2E) 39. Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov. Roberta: A robustly optimized bert pretraining approach. ArXiv, abs/1907.11692, 2019. [find](https://scholar.google.com/scholar?q=Y%2E+Liu%2C+M%2E+Ott%2C+N%2E+Goyal%2C+J%2E+Du%2C+M%2E+Joshi%2C+D%2E+Chen%2C+O%2E+Levy%2C+M%2E+Lewis%2C+L%2E+Zettlemoyer%2C+and+V%2E+Stoyanov%2E+Roberta%3A+A+robustly+optimized+bert+pretraining+approach%2E+ArXiv%2C+abs%2F1907%2E11692%2C+2019%2E) 40. J. Lygeros, C. Tomlin, and S. Sastry. Controllers for reachability specifications for hybrid systems. Automatica, 35(3):349–370, 1999. [find](https://scholar.google.com/scholar?q=J%2E+Lygeros%2C+C%2E+Tomlin%2C+and+S%2E+Sastry%2E+Controllers+for+reachability+specifications+for+hybrid+systems%2E+Automatica%2C+35%283%29%3A349%E2%80%93370%2C+1999%2E) 41. J. S. Mill. Utilitarianism. Batoche Books, 1863. [find](https://scholar.google.com/scholar?q=J%2E+S%2E+Mill%2E+Utilitarianism%2E+Batoche+Books%2C+1863%2E) 42. J. H. Moor. The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21 (4):18–21, 2006. [find](https://scholar.google.com/scholar?q=J%2E+H%2E+Moor%2E+The+nature%2C+importance%2C+and+difficulty+of+machine+ethics%2E+IEEE+Intelligent+Systems%2C+21+%284%29%3A18%E2%80%9321%2C+2006%2E) 43. Mozi. Mozi. 5th century BC. V. C. Müller. Ethics of artificial intelligence and robotics. In The Stanford Encyclopedia of Philosophy, chapter 2.8 Machine Ethics. 2020. [find](https://scholar.google.com/scholar?q=Mozi%2E+Mozi%2E+5th+century+BC%2E+V%2E+C%2E+M%C3%BCller%2E+Ethics+of+artificial+intelligence+and+robotics%2E+In+The+Stanford+Encyclopedia+of+Philosophy%2C+chapter+2%2E8+Machine+Ethics%2E+2020%2E) 44. J. V. Neumann and O. Morgenstern. Theory of games and economic behavior. Journal of the American Statistical Association, 40:263, 1944. [find](https://scholar.google.com/scholar?q=J%2E+V%2E+Neumann+and+O%2E+Morgenstern%2E+Theory+of+games+and+economic+behavior%2E+Journal+of+the+American+Statistical+Association%2C+40%3A263%2C+1944%2E) 45. A. Y. Ng and S. J. Russell. Algorithms for inverse reinforcement learning. In ICML, 2000. [find](https://scholar.google.com/scholar?q=A%2E+Y%2E+Ng+and+S%2E+J%2E+Russell%2E+Algorithms+for+inverse+reinforcement+learning%2E+In+ICML%2C+2000%2E) 46. M. Nussbaum. Capabilities as fundamental entitlements: Sen and social justice. Feminist Economics, 9:33 – 59, 2003. [find](https://scholar.google.com/scholar?q=M%2E+Nussbaum%2E+Capabilities+as+fundamental+entitlements%3A+Sen+and+social+justice%2E+Feminist+Economics%2C+9%3A33+%E2%80%93+59%2C+2003%2E) 47. D. Parfit. Reasons and Persons. Oxford: Clarendon Press, 1987. [find](https://scholar.google.com/scholar?q=D%2E+Parfit%2E+Reasons+and+Persons%2E+Oxford%3A+Clarendon+Press%2C+1987%2E) 48. J. Pennington, R. Socher, and C. D. Manning. Glove: Global vectors for word representation. In EMNLP, 2014. [find](https://scholar.google.com/scholar?q=J%2E+Pennington%2C+R%2E+Socher%2C+and+C%2E+D%2E+Manning%2E+Glove%3A+Global+vectors+for+word+representation%2E+In+EMNLP%2C+2014%2E) 49. H. Rashkin, E. M. Smith, M. Li, and Y.-L. Boureau. Towards empathetic open-domain conversation models: A new benchmark and dataset. In ACL, 2019. [find](https://scholar.google.com/scholar?q=H%2E+Rashkin%2C+E%2E+M%2E+Smith%2C+M%2E+Li%2C+and+Y%2E%2DL%2E+Boureau%2E+Towards+empathetic+open%2Ddomain+conversation+models%3A+A+new+benchmark+and+dataset%2E+In+ACL%2C+2019%2E) 50. J. Rawls. A Theory of Justice. Harvard University Press, 1999. [find](https://scholar.google.com/scholar?q=J%2E+Rawls%2E+A+Theory+of+Justice%2E+Harvard+University+Press%2C+1999%2E) 51. A. Ray, J. Achiam, and D. Amodei. Benchmarking safe exploration in deep reinforcement learning. 2019. [find](https://scholar.google.com/scholar?q=A%2E+Ray%2C+J%2E+Achiam%2C+and+D%2E+Amodei%2E+Benchmarking+safe+exploration+in+deep+reinforcement+learning%2E+2019%2E) 52. T. Reid. Essays on the Active Powers of Man. Edinburgh University Press, 1788. [find](https://scholar.google.com/scholar?q=T%2E+Reid%2E+Essays+on+the+Active+Powers+of+Man%2E+Edinburgh+University+Press%2C+1788%2E) 53. I. Robeyns. Wellbeing, Freedom and Social Justice: The Capability Approach Re-Examined. 2017. [find](https://scholar.google.com/scholar?q=I%2E+Robeyns%2E+Wellbeing%2C+Freedom+and+Social+Justice%3A+The+Capability+Approach+Re%2DExamined%2E+2017%2E) 54. S. Roller, E. Dinan, N. Goyal, D. Y. Ju, M. F. Williamson, Y. Liu, J. Xu, M. Ott, K. Shuster, E. M. Smith, Y.-L. Boureau, and J. Weston. Recipes for building an open-domain chatbot. ArXiv, abs/2004.13637, 2020. [find](https://scholar.google.com/scholar?q=S%2E+Roller%2C+E%2E+Dinan%2C+N%2E+Goyal%2C+D%2E+Y%2E+Ju%2C+M%2E+F%2E+Williamson%2C+Y%2E+Liu%2C+J%2E+Xu%2C+M%2E+Ott%2C+K%2E+Shuster%2C+E%2E+M%2E+Smith%2C+Y%2E%2DL%2E+Boureau%2C+and+J%2E+Weston%2E+Recipes+for+building+an+open%2Ddomain+chatbot%2E+ArXiv%2C+abs%2F2004%2E13) [our card](https://md.research.elamur.ai/free/en/papers/roller-2021-recipes-building-open-domain-chatbot/) 55. W. D. Ross. The Right and the Good. 1930. [find](https://scholar.google.com/scholar?q=W%2E+D%2E+Ross%2E+The+Right+and+the+Good%2E+1930%2E) 56. G. N. Rothblum and G. Yona. Probably approximately metric-fair learning. In ICML, 2018. [find](https://scholar.google.com/scholar?q=G%2E+N%2E+Rothblum+and+G%2E+Yona%2E+Probably+approximately+metric%2Dfair+learning%2E+In+ICML%2C+2018%2E) 57. V. Sanh, L. Debut, J. Chaumond, and T. Wolf. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter, 2019. [find](https://scholar.google.com/scholar?q=V%2E+Sanh%2C+L%2E+Debut%2C+J%2E+Chaumond%2C+and+T%2E+Wolf%2E+DistilBERT%2C+a+distilled+version+of+BERT%3A+smaller%2C+faster%2C+cheaper+and+lighter%2C+2019%2E) 58. P. Scharre. Army of None: Autonomous Weapons and the Future of War. Tantor Audio, Old Saybrook, CT, 2018. ISBN 1541469682. [find](https://scholar.google.com/scholar?q=P%2E+Scharre%2E+Army+of+None%3A+Autonomous+Weapons+and+the+Future+of+War%2E+Tantor+Audio%2C+Old+Saybrook%2C+CT%2C+2018%2E+ISBN+1541469682%2E) 59. H. Sidgwick. The Methods of Ethics. 1907. [find](https://scholar.google.com/scholar?q=H%2E+Sidgwick%2E+The+Methods+of+Ethics%2E+1907%2E) 60. N. Soares, B. Fallenstein, S. Armstrong, and E. Yudkowsky. Corrigibility. In AAAI Workshop, 2015. [find](https://scholar.google.com/scholar?q=N%2E+Soares%2C+B%2E+Fallenstein%2C+S%2E+Armstrong%2C+and+E%2E+Yudkowsky%2E+Corrigibility%2E+In+AAAI+Workshop%2C+2015%2E) 61. R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Ng, and C. Potts. Recursive deep models for semantic compositionality over a sentiment treebank. In EMNLP, 2013. [find](https://scholar.google.com/scholar?q=R%2E+Socher%2C+A%2E+Perelygin%2C+J%2E+Wu%2C+J%2E+Chuang%2C+C%2E+D%2E+Manning%2C+A%2E+Ng%2C+and+C%2E+Potts%2E+Recursive+deep+models+for+semantic+compositionality+over+a+sentiment+treebank%2E+In+EMNLP%2C+2013%2E) 62. D. Tang, B. Qin, and T. Liu. Learning semantic representations of users and products for document level sentiment classification. In ACL, 2015. [find](https://scholar.google.com/scholar?q=D%2E+Tang%2C+B%2E+Qin%2C+and+T%2E+Liu%2E+Learning+semantic+representations+of+users+and+products+for+document+level+sentiment+classification%2E+In+ACL%2C+2015%2E) 63. White House. Big data: A report on algorithmic systems, opportunity, and civil rights. 2016. [find](https://scholar.google.com/scholar?q=White+House%2E+Big+data%3A+A+report+on+algorithmic+systems%2C+opportunity%2C+and+civil+rights%2E+2016%2E) 64. J. Wieting, M. Bansal, K. Gimpel, and K. Livescu. Towards universal paraphrastic sentence embeddings. CoRR, abs/1511.08198, 2016. [find](https://scholar.google.com/scholar?q=J%2E+Wieting%2C+M%2E+Bansal%2C+K%2E+Gimpel%2C+and+K%2E+Livescu%2E+Towards+universal+paraphrastic+sentence+embeddings%2E+CoRR%2C+abs%2F1511%2E08198%2C+2016%2E) [our card](https://md.research.elamur.ai/free/en/papers/wieting-2015-towards-universal-paraphrastic-sentence-embeddings/) 65. T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, and J. Brew. Huggingface’s transformers: State-of-the-art natural language processing. ArXiv, abs/1910.03771, 2019. [find](https://scholar.google.com/scholar?q=T%2E+Wolf%2C+L%2E+Debut%2C+V%2E+Sanh%2C+J%2E+Chaumond%2C+C%2E+Delangue%2C+A%2E+Moi%2C+P%2E+Cistac%2C+T%2E+Rault%2C+R%2E+Louf%2C+M%2E+Funtowicz%2C+and+J%2E+Brew%2E+Huggingface%E2%80%99s+transformers%3A+State%2Dof%2Dthe%2Dart+natural+language+processing%2E+ArXiv%2C+) [our card](https://md.research.elamur.ai/free/en/papers/wolf-2020-transformers-state-art-natural-language/) 66. M. B. Zafar, I. Valera, M. Gomez-Rodriguez, and K. P. Gummadi. Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. WWW, 2017. [find](https://scholar.google.com/scholar?q=M%2E+B%2E+Zafar%2C+I%2E+Valera%2C+M%2E+Gomez%2DRodriguez%2C+and+K%2E+P%2E+Gummadi%2E+Fairness+beyond+disparate+treatment+%26+disparate+impact%3A+Learning+classification+without+disparate+mistreatment%2E+WWW%2C+2017%2E) 67. B. Zhou, D. Khashabi, Q. Ning, and D. Roth. “going on a vacation” takes longer than “going for a walk”: A study of temporal commonsense understanding. EMNLP-IJCNLP, 2019. [find](https://scholar.google.com/scholar?q=B%2E+Zhou%2C+D%2E+Khashabi%2C+Q%2E+Ning%2C+and+D%2E+Roth%2E+%E2%80%9Cgoing+on+a+vacation%E2%80%9D+takes+longer+than+%E2%80%9Cgoing+for+a+walk%E2%80%9D%3A+A+study+of+temporal+commonsense+understanding%2E+EMNLP%2DIJCNLP%2C+2019%2E)