Administrative Information
| Title | Trust, Normativity and Model Drift |
| Duration | 60 |
| Module | C |
| Lesson Type | Interactive Session |
| Focus | Technical - Future AI |
| Topic | Open Problems and Challenges |
Keywords
Trust, Model drift, Normativity,
Learning Goals
- Apply trust frameworks to several AI/ML example case studies
- Propose trust metrics to example case studies and link to EU Trust framework
- Discuss digital normativity in the context of example case studies
- Identify and Model Drift potential in example case studies and propose drift metrics and solutions to re-enforce model trust
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
Optional for Students
References and background for students
Recommended for Teachers
None.
Lesson materials
Instructions for Teachers
Instigate the students to engage in discussions. Lead and guide the discussion within the scope of the discussion points. Encourage students to focus on the evidence and interrupt if they are speaking over others. Keep a note of all the discussion points and share the discussion trail at the end of class. Provide conclusive remarks of the discussion with possible open questions and challenges.
Outline
| Duration (min) | Description |
|---|---|
| 5 | Problem Definition |
| 25 | Examples of Model drift, Types of Model Drift |
| 15 | Discussion around Normativity, Applying trust frameworks to AI/ML models |
| 5 | Summary of discussion on Model Drift and Normativity and open ended Questions |
Acknowledgements
The Human-Centered AI Masters programme was Co-Financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.