Fetching the paper…
Reading the bibliography…
As artificial intelligence (AI) models continue to scale up, they are becoming more capable and integrated into various forms of decision-making systems.
Piaget, J.: The Moral Judgement of The Child. Penguin (1932)
1932
Earlier work this paper cites.
Asch, S.E.: Studies of independence and conformity: I. a minority of one against a unanimous majority. Psychological Monographs: General and Applied 70
1956
Earlier work this paper cites.
Kohlberg, L.: Moral stages and moralization: the cognitive-development approach. Moral Development and Behavior: Theory, Research and Social Issues, 31–53 (1976)
1976
Earlier work this paper cites.
Searle, J.R.: Minds, brains, and programs. Behavioral and Brain Sciences 3
1980
Earlier work this paper cites.
Gauthier, D.: Morals by Agreement. Clarendon Press (1987). https://doi.org/10.1093/0198249926.001.0001
1987
Earlier work this paper cites.
Straughan, R.: Can we teach children to be good?: basic issues in moral, personal, and social education. McGraw-Hill Education (UK) (1988)
1988
Earlier work this paper cites.
Prior, W.J.: Can virtue be taught? Laetaberis: The Journal of the California Classical Association 8
1990
Earlier work this paper cites.
Vitell, S.J., Nwachukwu, S.L., Barnes, J.H.: The effects of culture on ethical decision-making: An application of hofstede’s typology. Journal of Business Ethics 12
1993
Earlier work this paper cites.
Skorupski, J.: The definition of morality. Royal Institute of Philosophy Supplements 35
1993
Earlier work this paper cites.
Provost, F.J., Hennessy, D.N.: Scaling up: Distributed machine learning with cooperation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 13 (1996)
1996
Earlier work this paper cites.
Rest, J.R., Thoma, S.J., Bebeau, M.J., et al.: Postconventional moral thinking: A neo-Kohlbergian approach. Psychology Press (1999)
1999
Earlier work this paper cites.
Allen, C., Varner, G., Zinser, J.: Prolegomena to any future artificial moral agent. Journal of Experimental & Theoretical Artificial Intelligence 12
2000
Earlier work this paper cites.
Haidt, J.: The emotional dog and its rational tail: a social intuitionist approach to moral judgment. Psychological Review 108
2001
Earlier work this paper cites.
Greene, J.D., Sommerville, R.B., Nystrom, L.E., Darley, J.M., Cohen, J.D.: An fmri investigation of emotional engagement in moral judgment. Science 293
2001
Earlier work this paper cites.
2001
Earlier work this paper cites.
Greene, J., Haidt, J.: How (and where) does moral judgment work? Trends in Cognitive Sciences 6
2002
Earlier work this paper cites.
Allen, C., Smit, I., Wallach, W.: Artificial morality: Top-down, bottom-up, and hybrid approaches. Ethics and Information Technology 7
2005
Earlier work this paper cites.
Tilly, C.: Historical perspectives on inequality. The Blackwell Companion to Social Inequalities, 15–30 (2005) https://doi.org/10.1002/9780470996973.ch2
2005
Earlier work this paper cites.
Moor, J.H.: The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems 21
2006
Earlier work this paper cites.
Haidt, J.: Morality. Perspectives on Psychological Science 3
2008
Earlier work this paper cites.
Haidt, J., Bjorklund, F.: Social intuitionists answer six questions about morality. Moral Psychology (2008). https://ssrn.com/abstract=855164
2008
Earlier work this paper cites.
Upton, C.L.: Virtue ethics and moral psychology: The situationism debate. The Journal of Ethics 13
2009
Earlier work this paper cites.
Kelly, K.: Out of control: The new biology of machines, social systems, and the economic world. Hachette UK (2009)
2009
Earlier work this paper cites.
Pearl, J.: Causality. Cambridge University Press (2009). https://doi.org/10.1017/CBO9780511803161
2009
Earlier work this paper cites.
Johansson, L.: The functional morality of robots. International Journal of Technoethics 1
2010
Earlier work this paper cites.
Kahneman, D.: Thinking, fast and slow. Macmillan (2011)
2011
Earlier work this paper cites.
Cribb, A., Entwistle, V.A.: Shared decision making: trade-offs between narrower and broader conceptions. Health Expectations 14
2011
Earlier work this paper cites.
Hardy, S.A., Carlo, G.: Moral identity: What is it, how does it develop, and is it linked to moral action? Child Development Perspectives 5
2011
Earlier work this paper cites.
Gibbs, J.C.: Moral Development and Reality: Beyond the Theories of Kohlberg, Hoffman, and Haidt. Oxford University Press (2013). https://doi.org/10.1093/acprof:osobl/9780199976171.001.0001
2013
Earlier work this paper cites.
Dietvorst, B.J., Simmons, J.P., Massey, C.: Algorithm aversion: people erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General 144
2015
Earlier work this paper cites.
Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M.: Machine learning: The high interest credit card of technical debt. In: NeurIPS 2014 Workshop on Software Engineering for Machine Learning (SE4ML) (2014). https://papers.nips.cc/paper_files/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf
2015
Earlier work this paper cites.
Cervantes, J.-A., Rodríguez, L.-F., López, S., Ramos, F., Robles, F.: Autonomous agents and ethical decision-making. Cognitive Computation 8
2016
Earlier work this paper cites.
Chouldechova, A.: Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data 5
2016
Earlier work this paper cites.
Feng, L., Wiltsche, C., Humphrey, L., Topcu, U.: Synthesis of human-in-the-loop control protocols for autonomous systems. IEEE Transactions on Automation Science and Engineering 13
2016
Earlier work this paper cites.
Tekin, C., Yoon, J., Van Der Schaar, M.: Adaptive ensemble learning with confidence bounds. IEEE Transactions on Signal Processing 65
2016
Earlier work this paper cites.
Mermet, B., Simon, G.: Formal verication of ethical properties in multiagent systems. In: 1st Workshop on Ethics in the Design of Intelligent Agents (2016). https://hal.science/hal-01708133/document
2016
Earlier work this paper cites.
Brutzman, D., Blais, C.L., Davis, D.T., McGhee, R.B.: Ethical mission definition and execution for maritime robots under human supervision. IEEE Journal of Oceanic Engineering 43
2017
Earlier work this paper cites.
Kusner, M.J., Loftus, J., Russell, C., Silva, R.: Counterfactual fairness, vol. 30 (2017). https://papers.nips.cc/paper_files/paper/2017/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html
2017
Earlier work this paper cites.
Lipton, Z.C.: The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16
2018
Earlier work this paper cites.
Garrigan, B., Adlam, A.L., Langdon, P.E.: Moral decision-making and moral development: toward an integrative framework. Developmental Review 49
2018
Earlier work this paper cites.
Mattingly, C., Throop, J.: The anthropology of ethics and morality. Annual Review of Anthropology 47
2018
Earlier work this paper cites.
Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D.P., Wilson, A.G.: Loss surfaces, mode connectivity, and fast ensembling of dnns. In: Advances in Neural Information Processing Systems, vol. 31 (2018). https://papers.nips.cc/paper_files/paper/2018/hash/be3087e74e9100d4bc4c6268cdbe8456-Abstract.html
2018
Earlier work this paper cites.
Jacot, A., Gabriel, F., Hongler, C.: Neural tangent kernel: Convergence and generalization in neural networks. In: Advances in Neural Information Processing Systems, vol. 31 (2018). https://papers.nips.cc/paper_files/paper/2018/hash/5a4be1fa34e62bb8a6ec6b91d2462f5a-Abstract.html
2018
Earlier work this paper cites.
Hoque, E.: Memorization: a proven method of learning. International Journal of Applied Research 22
2018
Earlier work this paper cites.
Dignum, V., Baldoni, M., Baroglio, C., Caon, M., Chatila, R., Dennis, L., Génova, G., Haim, G., Kließ, M.S., Lopez-Sanchez, M., et al
2018
Cited alongside, same era.
Shaw, N.P., Stöckel, A., Orr, R.W., Lidbetter, T.F., Cohen, R.: Towards provably moral ai agents in bottom-up learning frameworks. In: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pp. 271–277 (2018). https://doi.org/10.1145/3278721.3278728
2018
Cited alongside, same era.
Mostafa, S.A., Ahmad, M.S., Mustapha, A.: Adjustable autonomy: a systematic literature review. Artificial Intelligence Review 51
2019
Cited alongside, same era.
Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1
2019
Cited alongside, same era.
Chen, C., Lin, K., Rudin, C., Shaposhnik, Y., Wang, S., Wang, T.: A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations. Decision Support Systems 152
2021
Later among the works it cites.
2021
Later among the works it cites.
Chandu, K.R., Bisk, Y., Black, A.W.: Grounding ‘grounding’ in nlp. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 4283–4305 (2021). https://aclanthology.org/2021.findings-acl.375/
2021
Later among the works it cites.
Sousa Ribeiro, M., Leite, J.: Aligning artificial neural networks and ontologies towards explainable ai. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4932–4940 (2021). https://ojs.aaai.org/index.php/AAAI/article/view/16626
2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cai, C.J., Winter, S., Steiner, D., Wilcox, L., Terry, M.: “hello ai”: uncovering the onboarding needs of medical practitioners for human-ai collaborative decision-making. (2019). https://dl.acm.org/doi/10.1145/3359206
2019
Cited alongside, same era.
Ali, A.H.: A survey on vertical and horizontal scaling platforms for big data analytics. International Journal of Integrated Engineering 11
2019
Cited alongside, same era.
Obermeyer, Z., Powers, B., Vogeli, C., Mullainathan, S.: Dissecting racial bias in an algorithm used to manage the health of populations. Science 366
2019
Cited alongside, same era.
Ricaurte, P.: Data epistemologies, the coloniality of power, and resistance. Television & New Media 20
2019
Cited alongside, same era.
Bhaskaruni, D., Hu, H., Lan, C.: Improving prediction fairness via model ensemble. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1810–1814 (2019). https://doi.org/10.1109/ICTAI.2019.00273
2019
Cited alongside, same era.
Aggarwal, A., Lohia, P., Nagar, S., Dey, K., Saha, D.: Black box fairness testing of machine learning models. In: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 625–635 (2019). https://dl.acm.org/doi/10.1145/3338906.3338937
2019
Cited alongside, same era.
Rossi, F., Mattei, N.: Building ethically bounded ai. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 9785–9789 (2019). https://doi.org/10.1609/aaai.v33i01.33019785
2019
Cited alongside, same era.
Cervantes, J.-A., López, S., Rodríguez, L.-F., Cervantes, S., Cervantes, F., Ramos, F.: Artificial moral agents: A survey of the current status. Science and Engineering Ethics 26
2020
Cited alongside, same era.
Later among the works it cites.
Mhasawade, V., Chunara, R.: Causal multi-level fairness. In: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, pp. 784–794 (2021). https://dl.acm.org/doi/10.1145/3461702.3462587
2021
Later among the works it cites.
Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S.G., Novikov, A., Barth-maron, G., Giménez, M., Sulsky, Y., Kay, J., Springenberg, J.T., Eccles, T., Bruce, J., Razavi, A., Edwards, A., Heess, N., Chen, Y., Hadsell, R., Vinyals, O., Bordbar, M., Freitas, N.: A generalist agent. Transactions on Machine Learning Research (2022). https://openreview.net/forum?id=1ikK0kHjvj
2022
Later among the works it cites.
Ibarz, B., Kurin, V., Papamakarios, G., Nikiforou, K., Bennani, M., Csordás, R., Dudzik, A.J., Bošnjak, M., Vitvitskyi, A., Rubanova, Y., et al
2022
Later among the works it cites.
Jablonka, K.M., Schwaller, P., Smit, B.: Is gpt-3 all you need for machine learning for chemistry? In: NeurIPS 2022 Workshop on AI for Accelerated Materials Design (2022). https://openreview.net/forum?id=dgpgTEZ6G__
2022
Later among the works it cites.
Wang, Z., Wu, Z., Agarwal, D., Sun, J.: MedCLIP: Contrastive learning from unpaired medical images and text. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (2022). https://aclanthology.org/2022.emnlp-main.256
2022
Later among the works it cites.
Acosta, J.N., Falcone, G.J., Rajpurkar, P., Topol, E.J.: Multimodal biomedical ai. Nature Medicine 28
2022
Later among the works it cites.
Watson, D.S.: Conceptual challenges for interpretable machine learning. Synthese 200
2022
Later among the works it cites.
Hindocha, S., Badea, C.: Moral exemplars for the virtuous machine: the clinician’s role in ethical artificial intelligence for healthcare. AI and Ethics 2
2022
Later among the works it cites.
Post, B., Badea, C., Faisal, A., Brett, S.J.: Breaking bad news in the era of artificial intelligence and algorithmic medicine: an exploration of disclosure and its ethical justification using the hedonic calculus. AI and Ethics (2022) https://doi.org/10.1007/s43681-022-00230-z
2022
Later among the works it cites.
Jin, Z., Levine, S., Gonzalez Adauto, F., Kamal, O., Sap, M., Sachan, M., Mihalcea, R., Tenenbaum, J., Schölkopf, B.: When to make exceptions: Exploring language models as accounts of human moral judgment. In: Advances in Neural Information Processing Systems, vol. 35 (2022). https://openreview.net/forum?id=uP9RiC4uVcR
2022
Later among the works it cites.
Badea, C., Artus, G.: Morality, machines, and the interpretation problem: A value-based, wittgensteinian approach to building moral agents. In: Artificial Intelligence XXXIX: 42nd SGAI International Conference on Artificial Intelligence (2022). https://link.springer.com/chapter/10.1007/978-3-031-21441-7_9
2022
Later among the works it cites.
Badea, C.: Have a break from making decisions, have a mars: The multi-valued action reasoning system. In: Artificial Intelligence XXXIX: 42nd SGAI International Conference on Artificial Intelligence (2022). https://link.springer.com/chapter/10.1007/978-3-031-21441-7_31
2022
Later among the works it cites.
Fatumo, S., Chikowore, T., Choudhury, A., Ayub, M., Martin, A.R., Kuchenbaecker, K.: A roadmap to increase diversity in genomic studies. Nature Medicine 28
2022
Later among the works it cites.
Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., Hall, P., et al
2022
Later among the works it cites.
2022
Later among the works it cites.
Dai, J., Upadhyay, S., Aivodji, U., Bach, S.H., Lakkaraju, H.: Fairness via explanation quality: Evaluating disparities in the quality of post hoc explanations. In: Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, pp. 203–214 (2022). https://doi.org/10.1145/3514094.3534159
2022
Later among the works it cites.
Rodriguez-Soto, M., Serramia, M., Lopez-Sanchez, M., Rodriguez-Aguilar, J.A.: Instilling moral value alignment by means of multi-objective reinforcement learning. Ethics and Information Technology 24
2022
Later among the works it cites.
2022
Later among the works it cites.
Miller, G.J.: Stakeholder-accountability model for artificial intelligence projects. Journal of Economics and Management 44
2022
Later among the works it cites.
2022
Later among the works it cites.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E.H., Le, Q.V., Zhou, D., et al
2022
Later among the works it cites.
2022
Later among the works it cites.
Roy, K., Gaur, M., Rawte, V., Kalyan, A., Sheth, A.: Proknow: Process knowledge for safety constrained and explainable question generation for mental health diagnostic assistance. Frontiers Big Data 5
2022
Later among the works it cites.
2022
Later among the works it cites.
Geiger, A., Wu, Z., Lu, H., Rozner, J., Kreiss, E., Icard, T., Goodman, N., Potts, C.: Inducing causal structure for interpretable neural networks. In: International Conference on Machine Learning, vol. 39, pp. 7324–7338 (2022). https://proceedings.mlr.press/v162/geiger22a/geiger22a.pdf
2022
Later among the works it cites.
Sallam, M.: Chatgpt utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare 11
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M.S., Krishna, R.: Explanations can reduce overreliance on ai systems during decision-making. Proceedings of the ACM on Human-Computer Interaction 7
2023
Closest in time.
Srivastava, A., Saisubramanian, S., Paruchuri, P., Kumar, A., Zilberstein, S.: Planning and learning for non-markovian negative side effects using finite state controllers. In: AAAI Conference on Artificial Intelligence (AAAI) (2023). https://ojs.aaai.org/index.php/AAAI/article/view/26767
2023
Closest in time.
2023
Closest in time.
Ayers, J.W., Poliak, A., Dredze, M., Leas, E.C., Zhu, Z., Kelley, J.B., Faix, D.J., Goodman, A.M., Longhurst, C.A., Hogarth, M., Smith, D.M.: Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. JAMA Internal Medicine 183
2023
Closest in time.
Lee, P., Bubeck, S., Petro, J.: Benefits, limits, and risks of gpt-4 as an ai chatbot for medicine. New England Journal of Medicine 388
2023
Closest in time.
Nanda, N., Chan, L., Lieberum, T., Smith, J., Steinhardt, J.: Progress measures for grokking via mechanistic interpretability. In: International Conference on Learning Representations, vol. 11 (2023). https://openreview.net/forum?id=9XFSbDPmdW
2023
Closest in time.
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys 55
2023
Closest in time.
2023
Closest in time.