empfohlenes Vorwissen

The students should have a basic knowledge of information systems and be familiar with:

• Fundamentals of Strategic Management
• Fundamentals of Data Bases and Enterprise Modelling

Abstract

Artificial Intelligence (AI) is widely considered a generative technology that has the potential to have great impact on our society, economy, and ecology. Whether these impacts will be for worse or for better is up for discussion and depends on the actions of individuals, companies, and authorities worldwide towards the 18 UN Sustainable Development Goals.

Throughout the lecture series, students get familiar with concepts and theories that describe and explain AI companies, and learn about the design of Machine Learning-based applications. Do we need AI – or does AI solve our problems? What problems can machine learning effectively solve? What is the current impact of AI technologies on economy, society and ecology? How can we apply AI to a new domain or problem? What role do humans play in designing AI applications?

Building on fundamentals of information systems strategy and enterprise modelling, students reflect the impact of strategy and organizing in AI companies towards their ability to produce sustainable futures. We particularly investigate the generative capacity of data, tools, and (machine learning) models to produce such futures. Among others, we will cover the impact of biases in data and algorithms, explainability of AI applications, as well as accuracy, sovereignty, (inverse) scalability and framing of ML models. Throughout the entire module, we critically reflect impacts of managerial and algorithmic decision-making on sustainability, this includes impacts, for instance, on aspects of health and well-being (SDG 3), gender equality (SDG 5), or climate action (SDG 13).

Lehrinhalte

• AI Companies & Data-centric Thinking
• Sustainable Information Systems
• Strategy & AI Companies for Sustainable Futures
• Organization & AI Companies for Sustainable Futures
• Managing Machine Learning Projects for Sustainable Futures
• Building AI Applications
• Generativity and Boundaries from Digital Tools
• Generativity and Boundaries from Data
• Generativity and Boundaries from (ML) Models

Literaturangaben

• Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021). Managing artificial intelligence. MIS Quarterly, 45(3).
• Brynjolfsson, E., & Mcafee, A. (2017). Artificial intelligence, for real. Harvard Business Review, 1, 1-31.
• Brynjolfsson, E., Rock, D., & Syverson, C. (2018). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In The economics of artificial intelligence: An agenda (pp. 23-57). University of Chicago Press.
• Fürstenau, D., Baiyere, A., Schewina, K., Schulte-Althoff, M., and Rothe, H. (forthcoming). Extended Generativity Theory on Digital Platforms, Information Systems Research.
• Gregory, R. W., Henfridsson, O., Kaganer, E., & Kyriakou, H. (2021). The role of artificial intelligence and data network effects for creating user value. Academy of Management Review, 46(3), 534-551.
• Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.
• Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192-210.
• Russell, S., & Norvig, P. (2021). Artificial Intelligence, Global Edition: A Modern Approach. (4th ed.). Pearson Education.

Further literature will be provided during the course

didaktisches Konzept

This course follows a blended-learning approach. Students are expected to watch and reflect upon video lectures and read obligatory literature as part of their weekly preparation, regardless of their location. Classroom discussions will enable students to critically reflect on the newly acquired knowledge and discuss open questions with the lecturer.

empfohlenes Vorwissen

see lecture

Lehrinhalte

The tutorial complements the lecture in that students critically reflect topics of the lecture before applying their newly acquired knowledge to a case study in which they design a minimum viable product for an AI application.

The tutorial extends the content of the lecture. In the first third of the course, the tutorial largely focuses on description, explanation, and eventually critical reflection of core topics from the lecture in light of current cases, such as generation of text, images, videos, or sounds with machine learning. Thereafter, students will be guided towards their own AI application to solve a real-world problem linked to the Sustainable Development Goals. Following a step-by-step design-oriented process, students develop a business case for this AI applications and work towards a minimum viable product using agile project management techniques and low-code applications. They are asked to present their solution in verbal and written assignments.

Literaturangaben

see lecture

didaktisches Konzept

The didactical design for this tutorial is highly design-oriented and focuses on team work, critical case reflection, group discussions, presentations and a written assignment.