Administrative information

Learning Events to be Completed Before

Obligatory for Students

Ethics content from module A
Ethics content from module B

Optional for Students

Introduction to machine learning and deep learning concepts given in previous lectures

References and background for students

Recommended for Teachers

None.

Lesson materials

Instructions for Teachers

This lecture should focus on the concept of Trust about systems that employ AI and machine learning to make decisions. It should define trust and the characteristics of trust along with the agents trust. The lecture should provide a practical link to the proposed EU Trust framework for AI. The lecture should also introduce the concept of digital normativity and the problem of model drift, measuring and monitoring model drift in the context of trustworthy AI and machine learning.

The goal of this lecture is to discuss the concept of trust in the context of AI systems. The lecture should answer the question: What does it mean to trust, and how can we build trust in AI systems? The lecture should also discuss the concept of normativity in the context of AI and automated decision making systems, adding weight to the importance of trust. Finally, the lecture will discuss model drift, the types of model drift, metrics to measure model drift, and how to deal with model drift, which should demonstrate that trust should be constantly monitored.

Title	Trust, Normativity and Model Drift
Duration	45-60
Module	C
Lesson Type	Lecture
Focus	Technical - Future AI
Topic	Open Problems and Challenges

Duration	Description	Concepts	Activity	Material
5 min	What is trust?	Philosophy of trust, characterising trust, agents and patients of trust, socio-technical ecosystem, role of trust in knowledge	Taught session and examples	Lecture materials
15 min	Research Task Trust in AI	Open questions and review of an article	Lecture materials
10 min	Advent of Digital Normativity	Subjectivation, Desubjectivation, justified agency, explainable and normative agency	Taught session and examples	Lecture materials
15 min	Model Drift	What is model drift, types of model/concept drift (prediction, concept, data, upstream), Drift metrics (Population Stability Index, KL divergence, Wasserstein’s Distance), Dealing with model drift (monitoring, data quality, retraining, parameter tuning)	Taught session and examples	Lecture materials
5 min	Conclusion	Summary	Conclusions	Lecture materials

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Keywords

Learning Goals

Expected Preparation