2023

McKee, K. R. (2023). Human participants in AI research: Ethics and transparency in practice. arXiv preprint arXiv:2311.01254.

Agnew, W., McKee, K. R., Gabriel, I., Kay, J., Isaac, W., Bergman, S., El-Sayed, S., & Mohamed, S. (2023). Technologies of resistance to AI. In Equity and Access in Algorithms, Mechanisms, and Optimization.

McKee, K. R. & Botvinick, M. (2023). AI learns to encourage group cooperation by making new connections. Nature Human Behaviour, 7, 1618–1619.

McKee, K. R., Tacchetti, A., Bakker, M., Balaguer, J., Campbell-Gillingham, L., Everett, R., & Botvinick, M. (2023). Scaffolding cooperation in human groups with deep reinforcement learning. Nature Human Behaviour, 7, 1787–1796. [details here]

McKee, K. R., Bai, X., & Fiske, S. T. (2023). Humans perceive warmth and competence in artificial intelligence. iScience, 26(8), 107256. [details here]

Joyce, D. W., Kormilitzin, A., Hamer-Hunt, J., McKee, K. R., & Tomasev, N. (2023, August). Protocol for a Delphi consensus process for PARticipatory Queer AI Research in Mental Health (PARQAIR-MH). medRxiv.

Madhushani, U., McKee, K. R., Agapiou, J.P., Leibo, J. Z., Everett, R., Anthony, T., Hughes, E., Tuyls, K., & Duéñez-Guzmán, E. A. (2023). Heterogeneous Social Value Orientation leads to meaningful diversity in sequential social dilemmas. In Adaptive and Learning Agents Workshop at the 22nd International Conference on Autonomous Agents and MultiAgent Systems.

Li, Z., Lanctot, M., McKee, K. R., Marris, L., Gemp, I., Hennes, D., Muller, P., Larson, K., Bachrach, Y., & Wellman, M. P. (June, 2023). Combining tree-search, generative models, and Nash bargaining concepts in game-theoretic reinforcement learning. In Proc. of the 22nd International Conference on Autonomous Agents and MultiAgent Systems (pp. 2445-2447). [details here]

Gabriel, I. & McKee, K. R. (2023, April). How can we build human values into AI? Google DeepMind.

Weidinger, L.*, McKee, K. R.*, Everett, R., Huang, S., Zhu, T. O., Chadwick, M., Summerfield, S., & Gabriel, I. (2023). Using the Veil of Ignorance to align AI systems with principles of justice. Proceedings of the National Academy of Sciences, 120(18), e2213709120. [details here]

Kormilitzin, A., Tomasev, N., McKee, K. R., & Joyce, D. W. (2023). A participatory initiative to include LGBT+ voices in AI for mental health. Nature Medicine, 29, 10–11. [details here]

2022

Kramár, J., Eccles, T., Gemp, I., Tacchetti, A., McKee, K. R., Malinowski, M., Graepel, T., & Bachrach, Y. (2022). Negotiation and honesty in artificial intelligence methods for the board game of Diplomacy. Nature Communications, 13(7214), 1-15. [details here]

Lu, C., Kay, J., & McKee, K. R. (2022, June). Subverting machines, fluctuating identities: Re-learning human categorization. In Proc. of the 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 1005-1015). [details here]

McKee, K. R., Bai, X., & Fiske, S. T. (2022, May). Warmth and competence in human-agent cooperation. In Proc. of the 21st International Conference on Autonomous Agents and MultiAgent Systems (pp. 898-907). [details here]

Gemp, I., McKee, K. R., Everett, R., Duéñez-Guzmán, E. A., Bachrach, Y., Balduzzi, D., & Tacchetti, A. (2022, May). D3C: Reducing the price of anarchy in multi-agent learning. In Proc. of the 21st International Conference on Autonomous Agents and MultiAgent Systems (pp. 498-506). [details here]

McKee, K. R., Leibo, J. Z., Beattie, C., & Everett, R. (2022). Quantifying the effects of environment and population diversity in multi-agent reinforcement learning. Autonomous Agents and Multi-Agent Systems, 36(21), 1–16. [details here]

2021

Kopparapu, K., Duéñez-Guzmán, E.A., Matyas, J., Vezhnevets, A. S., Agapiou, J. P., McKee, K. R., Everett, R., Marecki, J., Leibo, J. Z. and Graepel, T. (2021, December). Hidden Agenda: A social deduction game with diverse learned equilibria. In Cooperative AI workshop at the 2021 Conference on Neural Information Processing Systems. [details here]

Strouse, D., McKee, K. R., Botvinick, M., Hughes, E., & Everett, R. (2021, December). Collaborating with humans without human data. In Advances in Neural Information Processing Systems. [details here]

McKee, K. R., Hughes, E., Zhu, T. O., Chadwick, M., Koster, R., Castañeda, A. G., Beattie, C., Graepel, T., Botvinick, M., & Leibo, J. Z. (2021). Deep reinforcement learning models the emergent dynamics of human cooperation. arXiv preprint arxiv:2103.04982. [details here]

Tomasev, N., McKee, K. R., Kay, J., & Mohamed, S. (2021). Fairness for unobserved characteristics: Insights from technological impacts on queer communities. In Proc. of the AAAI/ACM Conference on AI, Ethics, and Society (AIES 2021) (pp. 254-265). [details here]

Moreno, P., Hughes, E., McKee, K. R., Pires, B. A., & Weber, T. (2021). Neural recursive belief states in multi-agent reinforcement learning. arXiv preprint arXiv:2102.02274. [details here]

2020

Dafoe, A., Hughes, E., Bachrach, Y., Collins, T., McKee, K. R., Leibo, J. Z., Larson, K., & Graepel, T. (2020). Open problems in Cooperative AI. arXiv preprint arXiv:2012.08630. [details here]

McKee, K. R., Gemp, I., McWilliams, B., Duèñez-Guzmán, E. A., Hughes, E., & Leibo, J. Z. (2020, May). Social diversity and social preferences in mixed-motive reinforcement learning. In Proc. of the 19th International Conference on Autonomous Agents and MultiAgent Systems (pp. 869-877). [details here]

2019

Zhu, T. O., Hamrick, J. B., McKee, K. R., Koster, R., Balaguer, J., Battaglia, P. W., & Botvinick, M. (2019, July). A resource-rational model of physical abstraction for efficient mental simulation. In Proc. of the 41st Annual Conference of the Cognitive Science Society (p. 3618). [details here]

2018

Hughes, E., Leibo, J. Z., Phillips, M.G., Tuyls, K., Duéñez-Guzmán, E. A., Castañeda, A. G., Dunning, I., Zhu, T. O., McKee, K. R., Koster, R., Roff, H., and Graepel, T. (2018, December). Inequity aversion improves cooperation in intertemporal social dilemmas. In Advances in Neural Information Processing Systems (pp. 3330-3340). [details here]

Hamrick, J., Allen, K., Bapst, V., Zhu, T. O., McKee, K. R., Tenenbaum, J. and Battaglia, P. (2018, July). Relational inductive bias for physical construction in humans and machines. In Proc. of the 40th Annual Conference of the Cognitive Science Society (pp. 1773-1778). [details here]