ArtificiaI Intelligence and societal impact

AI is revolutionizing society, but can you tell opportunity from hype? Understand the social and technical sides 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 revolutionise society, improving efficiency in nearly aspect 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 work, healthcare, and public safety. We pair these seminars with hands-on workshops on the technical side of AI. While no prior coding experience is required, you and a team of your peers will train an algorithm to identify objects within an image.

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 analyse AI in relation to society.

The main objectives of this minor are for you to:

1. Understand the technical side of AI
2. Familiarise 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 employ a “flipped classroom” approach, with readings/videos completed outside class and groupwork in class. Each seminar will culminate in a group report. These assessments 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 utilises a hands-on approach to demystify the programming of algorithms.

Teaching materials
Academic literature, video lectures, news reports, coding sessions, field trips

Methods of examination
Seminar tracks will culminate in group assessments tied to the seminar theme (public safety, work, healthcare). These assessments will generally be written reports, requiring desk research, field research, and critical analysis. Each of these reports 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.
Finally, students will complete a group presentation, synthesizing lessons about AI from across the course.

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