Opinto-opas

Tavoitteet

For students who are new to Data Science and AI, the course aims to provide them with an introduction to the field, as well as the opportunity to engage with it in a hands-on fashion. For all students (including those with a Data Science/AI background), the course also aims to provide them with an understanding of the changes that the field is currently undergoing, especially from a regulatory, legal, and ethical perspective.

Sisältö

The course is structured around the various applications of ‘AI’, and in particular, the level of risk associated with each domain category: High Risk, High Stakes, and High Noon. This will be preceded by a general introduction to ‘AI’, and followed by a lighthearted ‘pub quiz’, where students try to link ethical AI imperatives to famous philosophers and scientists and their ideas.

1. Introduction to AI Systems
2. High Risk AI Systems
3. High Stakes AI Systems
4. High Noon AI Systems
5. The Only Way is Ethics (Pub Quiz)

An overview of each of these modules is provided in the sections below.

Introduction to AI Systems

Learning Outcomes:
By the end of this module, the student will understand what is meant by “AI Systems”, whether they qualify as “intelligent”, how they are different from traditional software developed via computer programming, and finally, why they require bespoke governance and regulation.

Topics Addressed:
- What are 'AI' systems?
- Are they really 'intelligent'?
- How are they different?
- Why do they need regulating?

High Risk AI Systems

Learning Outcomes:
By the end of this module, the student will understand what is meant by “High Risk AI Systems” under EU Law, how these systems will be regulated within the EU, and what organisations need to do in order to be compliant with EU Law when developing and deploying such systems.

Topics Addressed:
- What are High Risk AI Systems?
- How will they be regulated? (EU AI Act, etc.)
- What do you need to do to comply?
- What does a 'compliant' AI system look like?

High Stakes AI Systems

Learning Outcomes: By the end of this module, the student will understand what is meant by “High Stakes AI Systems”, why they require organisations to go beyond EU Law, the difference between correlation and causation, and applying best practice from Science.

Topics Addressed:
- What are High Stakes AI Systems?
- Pattern identification and interpretation
- From correlation to causation
- From 'doing data' to 'doing science'

High Noon AI Systems

Learning Outcomes:
By the end of this module, the student will understand what is meant by “High Noon AI Systems”, why using such systems to manipulate human behaviour raises profound ethical questions, and some of the real-world impacts on both individuals and societies.

Topics Addressed:
- What are High Noon AI Systems?
- When humans are the product
- Ethics of modifying human behaviour
- Psychological, social and political impacts

The Only Way is Ethics (Pub Quiz)
A lighthearted look at how modern, ethical dilemmas in AI are often prefigured by earlier religious, philosophical, and scientific ideas. Can you link the AI ethical imperative to a historical figure and their contribution to the canon of human wisdom? A team competition, where Googling (or ChatGTP, etc.) is positively encouraged!

Opetusmenetelmät

The course will consist of four modules of 4 hours duration, plus a ‘pub-quiz’ (in the final session. During the lectures/workshops, students will also gain hands-on experience of Data Science practice, using the WEKA machine learning platform (https://en.wikipedia.org/wiki/Weka_(machine_learning)).

Arviointimenetelmät ja arvioinnin perusteet

At the end of each lecture/workshop, there will be a short quiz (typically, multiple-choice), to assess students’ knowledge of the module content. Scores will be aggregated at the end of the course (together with the ‘pub quiz’), to provide an overall score for the course. Score ranges will be translated into a three-tier marking system: Fair, Good, and Excellent.

Esitietovaatimukset

There are no prerequisites for taking the course, which is primarily aimed at students from non-Data Science/AI disciplines. (However, the course should also be of interest to Data Science/AI students, as much of the course content is not generally included in undergraduate Data Science/AI programmes).