• Graduate Program
    • Why study Business Data Science?
    • Program outline
    • Courses
    • Course registration
    • Admissions
    • Facilities
      • Student Offices
      • Location
      • Housing
      • Student Council
  • Research
  • News
  • Events
    • Events Calendar
    • Events archive
    • Summer School
      • Behavioral Decision Making
      • Deep Learning
      • Foundations of Data Analysis and Machine Learning in Python
      • Introduction to Genome-Wide Data Analysis
      • Reinforcement Learning
      • Tinbergen Institute Summer School Program
  • Alumni
Home | Courses | AI@Work


  • Teacher(s)
    Marleen Huysman, Wendy Günther, Mohammad Rezazade Mehrizi, Elmira van den Broek, Ella Hafermalz, Anastasia Sergeeva
  • Research field
    Management Science
  • Dates
    Period 1 - Aug 30, 2021 to Oct 22, 2021
  • Course type
  • Program year
  • Credits

Course description

This course encourages students to reflect on the impact of AI on (knowledge) work, to understand how AI can effectively and responsibly be implemented and managed in organizations. During the course, students will be exposed to concepts and theories from the business disciplines.

Course literature

The following list of mandatory readings are considered essential for your learning experience. Changes in the reading list will be communicated on Canvas.

  • Waardenburg, L., Huysman, M., & Agterberg, M. (2021) “Managing AI Wisely”, New Horizons in Business Analytics, Cheltenham, UK: Edward Elgar Publishing
  • Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society. https://doi.org/10.1177/2053951715622512
  • Floridi, L. (2019). Establishing the rules for building trustworthy AI. Nature Machine Intelligence, 1(6), 261-262. https://philpapers.org/archive/FLOETR.pdf
  • Veale, M. (2020). A Critical Take on the Policy Recommendations of the EU High-Level Expert Group on Artificial Intelligence https://discovery.ucl.ac.uk/id/eprint/10084302/7/Veale_HLEG_preprint_revised.pdf
  • .European Commission (2019) Ethics Guidelines for Trustworthy AI https://www.aepd.es/sites/default/files/2019-12/ai...guidelines.pdf
  • Anthony, C. (2021). When Knowledge Work and Analytical Technologies Collide: The Practices and Consequences of Black Boxing Algorithmic Technologies. Administrative Science Quarterly. https://doi.org/10.1177/00018392211016755
  • Beane, M. (2019). Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail. Administrative Science Quarterly, 64(1), 87-123.
  • Balasubramanian, N., Ye, Y., & Xu, M. (2020). Substituting Human Decision-Making with Machine Learning: Implications for Organizational Learning. Academy of Management Review, November. https://doi.org/10.5465/amr.2019.0470.
  • Waardenburg, L., Sergeeva, A., & Huysman, M. (2021). In the land of the blind, the one-eyed man is king: Knowledge brokerage in the age of learning algorithms. Manuscript under review
  • Pawlowski, S.D. & Robey, D. (2004). Bridging user organizations: Knowledge brokering and the work of information technology professionals. MIS Quarterly, 28(4), 645–672
  • Carlile, P. R. (2004). Transferring, translating, and transforming: An integrative framework for managing knowledge across boundaries. Organization Science, 15(5), 55-5568
  • Forsythe, D.E. (1993). The construction of work in artificial intelligence. Science Technology, and Human Values 18(4), 460– 479.
  • Barocas, S. & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104(3), 671-732.
  • Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399.
  • Moser, C., den Hond, F., & Lindebaum, D. (2021). Morality in the age of artificially intelligent algorithms. Academy of Management Learning & Education, https://doi.org/10.5465/amle.2020.0287
  • Van den Broek, E., Huysman, M., & Sergeeva, A. (2021, September). Shaping moral values with AI: A process study of fairness in hiring. [Paper presentation]. 12th International Process Symposium, Rhodos.
  • Günther, W. A., Rezazade-Mehrizi, M., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. Journal of Strategic Information Systems, 26, 191–209.
  • Hartmann, P. M., Zaki, M., Feldmann, N., & Neely, A. (2016). Capturing value from big data - A taxonomy of data-driven business models used by start-up firms. International Journal of Operation & Production Management, 36, 1382–1406.
  • Zeng, J., & Glaister, K. W. (2017). Value creation from big data: Looking inside the black box. Strategic Organization, 16(2), 105–140.
  • Newell, S. & Marabelli, M. (2015). “Strategic Opportunities (and Challenges) of Algorithmic Decision-Making: A Call for Action on the Long-term Societal Effects of ‘Datafication’.” Journal of Strategic Information Systems, 24(1), 3–14