ArtificiaI Intelligence and societal impact 15 EC

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

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 technical harms this technology also causes. 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 holistic minor will help you to critically evaluate conversations around AI, helping you challenge positive and negative assumptions around this technology you may encounter in the future. It will give you the vocabulary, knowledge and various perspectives needed to grasp and analyze AI in relation to society.

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

​​​​​​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

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.

Teaching methods
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.

Teaching materials
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.

Methods of examination
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.
Students will have the opportunity to showcase their work at a multi-institution conference in week 10, celebrating AI-related minors at EUR, EMC, and TUD.

Composition of final grade
Week 3 assessment: 10%
Week 6 assessment: 10%
Week 9 assessment: 10%
Final portfolio: 40 %
Data challenge: 30%