• Zhi-Xuan T, Carroll M, Franklin M and Ashton H. (2024). Beyond Preferences in AI Alignment. Philosophical Studies. 10.1007/s11098-024-02249-w.

    https://link.springer.com/10.1007/s11098-024-02249-w

  • He F, Hu X, Qian X, Zhu Z and Ramani K. (2024). AdapTUI: Adaptation of Geometric-Feature-Based Tangible User Interfaces in Augmented Reality. Proceedings of the ACM on Human-Computer Interaction. 8:ISS. (44-69). Online publication date: 24-Oct-2024.

    https://doi.org/10.1145/3698127

  • White D. (2024). Automated Influence and Value Collapse. American Philosophical Quarterly. 10.5406/21521123.61.4.06. 61:4. (369-386). Online publication date: 1-Oct-2024.

    https://scholarlypublishingcollective.org/apq/article/61/4/369/391696/Automated-Influence-and-Value-CollapseResisting

  • Stray J, Halevy A, Assar P, Hadfield-Menell D, Boutilier C, Ashar A, Bakalar C, Beattie L, Ekstrand M, Leibowicz C, Moon Sehat C, Johansen S, Kerlin L, Vickrey D, Singh S, Vrijenhoek S, Zhang A, Andrus M, Helberger N, Proutskova P, Mitra T and Vasan N. (2024). Building Human Values into Recommender Systems: An Interdisciplinary Synthesis. ACM Transactions on Recommender Systems. 2:3. (1-57). Online publication date: 30-Sep-2024.

    https://doi.org/10.1145/3632297

  • Chee J, Kalyanaraman S, Ernala S, Weinsberg U, Dean S and Ioannidis S. Harm Mitigation in Recommender Systems under User Preference Dynamics. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (255-265).

    https://doi.org/10.1145/3637528.3671925

  • Zhu Z, Qin R, Huang J, Dai X, Yu Y, Yu Y and Zhang W. (2024). Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems. ACM Transactions on Information Systems. 42:4. (1-32). Online publication date: 31-Jul-2024.

    https://doi.org/10.1145/3637869

  • Ruan Q, Xu J, Leavy S, Mac Namee B and Dong R. Rewriting Bias: Mitigating Media Bias in News Recommender Systems through Automated Rewriting. Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization. (67-77).

    https://doi.org/10.1145/3627043.3659541

  • Brinkmann L, Baumann F, Bonnefon J, Derex M, Müller T, Nussberger A, Czaplicka A, Acerbi A, Griffiths T, Henrich J, Leibo J, McElreath R, Oudeyer P, Stray J and Rahwan I. (2023). Machine culture. Nature Human Behaviour. 10.1038/s41562-023-01742-2. 7:11. (1855-1868).

    https://www.nature.com/articles/s41562-023-01742-2

  • Bezou-Vrakatseli E, Brückner B and Thorburn L. SHAPE: A Framework for Evaluating the Ethicality of Influence. Multi-Agent Systems. (167-185).

    https://doi.org/10.1007/978-3-031-43264-4_11

  • Gilbert T, Lambert N, Dean S, Zick T, Snoswell A and Mehta S. Reward Reports for Reinforcement Learning. Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society. (84-130).

    https://doi.org/10.1145/3600211.3604698

  • Kasirzadeh A and Evans C. User Tampering in Reinforcement Learning Recommender Systems. Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society. (58-69).

    https://doi.org/10.1145/3600211.3604669

  • Stray J, Iyer R and Puig Larrauri H. (2023). The Algorithmic Management of Polarization and Violence on Social Media. SSRN Electronic Journal. 10.2139/ssrn.4429558.

    https://www.ssrn.com/abstract=4429558

  • Wang S, Ji H, Yin M, Wang Y, Lu M and Sun H. (2022). Enhanced Graph Learning for Recommendation via Causal Inference. Mathematics. 10.3390/math10111881. 10:11. (1881).

    https://www.mdpi.com/2227-7390/10/11/1881