GAMA Lab

In this lab we explore the organizational and ethical implications of generative artificial intelligence (AI) technologies. Generative AI technologies are reshaping how humans work and interact, how processes and teams coordinate, and how organizations are structured and managed. These powerful technologies offer incredible promise, but with them come new challenges and risks. The GAMA Lab looks for ways that managers can be a “force for good” in navigating these technologies in the age of AI.

Selected GAMA Lab Publications:

Berente, N., Kormylo, C., & Rosenkranz, C. (2024). Test-driven ethics for machine learning. Communications of the ACM, 67(5), 45-47.

Grisold, T., Berente, N., & Seidel, S. (2025). Guardrails for Human-AI Ecologies: Norm-Based Coordination and Design for Predictability. Management Information Systems Quarterly.

Kormylo, C., Adjerid, I., Ball, S., & Dogan, C. (2025). Till Tech Do Us Part: Betrayal Aversion and Its Role in Algorithm Use. Management Science.

Qu, X., Lotfi, A., & Jiang, Z. (2018). Predicting Time to Upgrade under Successive Product Generations: A Survival Model with Exponential-Decay Baseline Function. International Conference on Information Systems.

Seidel, S., Berente, N., Lindberg, A., Lyytinen, K., Martinez, B., & Nickerson, J. V. (2020). Artificial intelligence and video game creation: A framework for the new logic of autonomous design. Journal of Digital Social Research, 2(3), 126-157.

Seidel, S., Berente, N., Martinez, B., Lindberg, A., Lyytinen, K., & Nickerson, J. V. (2018). Autonomous tools in system design: Reflective practice in Ubisofts Ghost Recon Wildlands project. IEEE Computer, 51(10), 16-23.

Seidel, S., Berente, N., Lindberg, A., Lyytinen, K., & Nickerson, J. V. (2019). Autonomous tools and design: a triple-loop approach to human-machine learning. Communications of the ACM, 62(1), 50-57.

Xu, H., & Zhang, N. (2024). Goal orientation for fair machine learning algorithms. Production and Operations Management, 10591478241234998.