Publications of Nils Köbis

Köbis, N., Bonnefon, J.-F., & Rahwan, I. (2021). Bad machines corrupt good morals (TSE Working Papers No. 1212). Toulouse School of Economics.

Köbis, N., Lorenz-Spreen, P., Ajaj, T., Bonnefon, J.-F., Hertwig, R., & Rahwan, I. (2023). Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners. arXiv, 2306.04484 . https://doi.org/10.48550/arXiv.2306.04484

Purcell, Z. A., Dong, M., Nussberger, A.-M., Köbis, N., & Jakesch, M. (2023). Fears about AI-mediated communication are grounded in different expectations for one’s own versus others’ use. arXiv, 2305.01670 .

Starke, C., Kieslich, K., Reichert, M., & Köbis, N. (2023). Algorithms against corruption: A conjoint study on designing automated twitter posts to encourage collective action. SocArXiv, January 13, 2023. https://doi.org/10.31235/osf.io/wf45t

Köbis, N., Dorrough, A., Irlenbusch, B., Shalvi, S., & Gloeckner, A. (2022). Bribery across nations. Research Square, May 25, 2022. https://doi.org/10.21203/rs.3.rs-1690288/v1

Köbis, N., Oded, S., de Bruijn, A. L., Huang, S., & van Rooij, B. (2022). Is less more? Field evidence on the impact of anti-bribery policies on employee knowledge and corrupt behavior. SSRN eLibrary, October 21, 2022. https://doi.org/10.2139/ssrn.4255148

Lange, J., Köbis, N., & Herrmann, C. (2022). A systematic literature review and integrative framework on the link between emotions and corruption. OSF Preprints, June 15, 2022. https://doi.org/10.31219/osf.io/8uzkf

von Schenk, A., Klockmann, V., Bonnefon, J.-F., Rahwan, I., & Köbis, N. (2022). Lie detection algorithms attract few users but vastly increase accusation rates. arXiv, December 8, 2022. https://doi.org/10.48550/arXiv.2212.04277

von Schenk, A., Klockmann, V., & Köbis, N. (2022). Social preferences towards machines and humans. SSRN eLibrary, June 24, 2022. https://doi.org/10.2139/ssrn.4145868

Köbis, N., Starke, C., & Rahwan, I. (2021). Artificial intelligence as an anti-corruption tool (AI-ACT): Potentials and pitfalls for top-down and bottom-up approaches. arXiv, 2102.11567.