ArtificiaI Intelligence and Societal Impact (30 EC)

AI is transforming society, but can you tell opportunity from hype? This class focuses on the social, technical, and ethical impacts of AI.

Note: this is the 30-EC version of the minor ArtificiaI Intelligence and Societal Impact. It's also possible to follow the minor for 15 EC.

Since the launch of ChatGPT in late 2022, the conversation around artificial intelligence (AI) has changed significantly. Many say AI has the power to revolutionize society, improving efficiency in nearly all aspects of life. But critics highlight the environmental, ethical, and even psychological harms this technology can cause. How can you learn to distinguish hype from reality?

This minor offers an opportunity to become “fluent” in discussions around AI. We’ll cover both technical and societal views of AI, equipping you with multiple techniques to understand this powerful technology. Through lectures and field research, you’ll learn about how AI is changing public safety, healthcare, and work. We pair these seminars with hands-on workshops to teach you about the technical side of AI. While no prior coding experience is required, you will train an algorithm to perform image analysis and identification.

This version of AI and Societal Impact spans 2 terms, running from September through January. The first term (10 weeks) provides a holistic experience to help you challenge positive and negative assumptions around AI. The second term (8 weeks) centres on a capstone project, applying the critical approach gained in the first half of the course to a real-world problem involving AI. As you tackle these challenges, you will receive weekly supervision from the course coordinator, who will provide you will design thinking strategies to help you refine your approach. When available, you will also work with a faculty supervisor with relevant expertise for your capstone project.

The main objectives of this minor are for you to:

1. Understand the technical side of AI
2. Familiarize yourself with different perspectives on AI
3. Develop a critical attitude towards AI in relation to society
4. Apply a critical perspective and design thinking skills to a real-world challenge related to AI

After completing the minor, you should have a critical comprehension of:

• Basic AI principles
• The technical underpinning of AI
• Different perspectives on AI in society
• Design thinking strategies
• Real-world challenges of AI and society

Prior knowledge on and experience in the field of AI is not required, but a critical and creative outlook on the subject is encouraged. Those with prior knowledge and experience will be challenged accordingly.
This course is one of the Convergence minors on AI. You can find out about related minors at TU Delft (Engineering with AI), Erasmus MC (Neuroscience and AI) or Leiden (AI in Society) at this website.

Teaching Methods
During term 1, seminar sessions utilize a “flipped classroom” approach, with readings/videos completed outside class and discussion and groupwork in class. Each seminar culminates in a group assignment, which will help you develop field research, desk research, and video presentation skills.
Meanwhile, the workshops will teach you fundamental technical topics in AI. You’ll meet weekly in a computer lab, working with actual code to train an algorithm. This section of the course requires no prior coding experience, but utilizes a hands-on approach to demystify the programming of algorithms.
During term 2, the initial weeks will utilize lectures and group activities to understand design thinking. Later weeks will focus on capstone projects, under the supervision of relevant faculty and the course coordinator.

Teaching Materials
During term 1, experts from each domain (public safety, healthcare, work, and programming) will provide relevant academic literature and exercises to help you achieve the course objectives. Additionally, you will attend mandatory weekly tutorials to help you synthesize the different sections of the class and reflect on your journey. In total, class meets 3 times each week, with 105-minute time slots dedicated to sociological topics, technical topics, and synthesis, respectively.
During term 2, you will initially focus on exercises to develop design thinking skills and positive team dynamics. Later weeks will shift toward regular check-ins with relevant a supervisor(s).

Method of Examination
During term 1, seminar tracks will culminate in group assessments tied to the seminar theme (public safety, work, healthcare). These assessments will consist of written reports—requiring desk research, field research, and critical analysis—presentations, or other more creative approaches. Each of these assignments counts for 10% of the final grade.
The workshops will include regular ungraded deliverables to help students monitor their progress. The term will conclude with a final data challenge, in which groups train an algorithm to identify objects in an image. Groups will be graded on how well their algorithm works, its biases, and the groups’ rationale for their approach.
During term 2, students will initially complete ungraded exercises to build their expertise in design thinking as they complete work on their final capstone. In weeks 13 and 16, students will complete Pass/Fail checks, each accounting for 10% of the final grade. The final deliverable will take on a format of the students’ choosing that is defined in discussion with the course coordinator to achieve the learning objective. This will be evaluated through showcase presentations. Students will supplement this material with pass/fail reflections that occur as noted in weeks 13 and 16.

Composition final grade
Term 1:
Week 3 assessment: 10%
Week 6 assessment: 10%
Week 9 assessment 10%
Final portfolio: 40%
Data challenge: 30%
Part B:
Week 13 assessment: 10%
Week 16 assessment: 10%
Final project deliverable: 60%
Reflection: 20%