SingleView of Module
Module (6 Credits)
Towards Sustainable Futures with AI
- Name in diploma supplement
- Towards Sustainable Futures with AI
- Responsible
- Admission criteria
- See exam regulations.
- Workload
- 180 hours of student workload, in detail:
- Attendance: 60 hours
- Preparation, follow up: 60 hours
- Exam preparation: 60 hours
- Duration
- The module takes 1 semester(s).
- Qualification Targets
Students will be able to
- reflect on data-centric thinking in companies
- explain the difference between types of tasks for AI and multiple machine learning techniques
- apply machine learning techniques with low-code tools and are familiar with current models and libraries.
- understand and apply theories of strategy and organization to AI companies
- understand generative properties and mechanisms of information systems, especially AI applications
- explain and critically reflect the impact of characteristics of digital resources, including data, digital tools, and (machine learning) models on AI applications.
- explain and critically reflect the impact of information systems, particularly AI applications, on multiple sustainable development goals
- describe fundamental processes, methods, and tools producing AI applications
- describe and apply fundamental methods of ML project management.
- design a business case for an AI application and produce a minimum-viable product
- apply text generation and image generation models in assignments and reflect on their use
- Module Exam
Die Modulnote ergibt sich aus einer modulbezogen zusammengesetzten Prüfung in der Gestalt einer Klausur (in der Regel: 60-90 Minuten, 50% der Note) sowie einer Hausarbeit (20-30 Seiten, 50% der Note)
Prüfungsvorleistung: Zwei mündliche Testate von müssen bestanden werden und sind als Prüfungsvorleistung Zulassungsvoraussetzung zur Modulprüfung. Bestandene Testate haben nur Gültigkeit für die Prüfungen, die zu der Veranstaltung im jeweiligen Semester gehören. Die genauen Formalia werden in der ersten Sitzung bekannt gegeben.
- Usage in different degree programs
- Elements
Lecture (3 Credits)
Towards Sustainable Futures with AI
- Name in diploma supplement
- Towards Sustainable Futures with AI
- Organisational Unit
- Lecturers
- SPW
- 2
- Language
- English
- Cycle
- summer semester
- Participants at most
- no limit
- Preliminary knowledge
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).
- Contents
- 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
- Literature
- 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
- Teaching concept
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.
- Participants
Exercise (3 Credits)
Towards Sustainable Futures with AI
- Name in diploma supplement
- Towards Sustainable Futures with AI
- Organisational Unit
- Lecturers
- SPW
- 2
- Language
- English
- Cycle
- summer semester
- Participants at most
- no limit
- Preliminary knowledge
see lecture
- Contents
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.
- Literature
see lecture
- Teaching concept
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.
- Participants