Administrative information

Title	Trust Models and Trust quantification
Duration	90 min
Module	C
Lesson Type	Lecture
Focus	Technical - Future AI
Topic	Emerging Evaluations for HCAI models

Keywords

Trustworthiness,Trust,

Learning Goals

Understand trust as an AI Virtue
Understand the Assessment List for Trustworthy AI (ALTAI)
Be able to conduct a self-assessment to the ALTAI list

Expected Preparation

Learning Events to be Completed Before

Lecture: Strengths and Limitations of existing laws - a deeper dive

Obligatory for Students

None.

Optional for Students

ALTAI list

References and background for students

Floridi, L. (2021). Establishing the rules for building trustworthy AI. Ethics, Governance, and Policies in Artificial Intelligence, 41-45.

Recommended for Teachers

(AI HLEG) High-Level Expert Group on Artificial Intelligence, Ethics guidelines for trustworthy AI, European Commission, Text, Apr. 2019. Accessed: Oct. 26, 2020.
Zerilli, J., Bhatt, U., & Weller, A. (2022). How transparency modulates trust in artificial intelligence. Patterns.

Lesson materials

Lecture slides

The materials of this learning event are available under CC BY-NC-SA 4.0.

Instructions for Teachers

Introduction (10 min)
European Approach to AI Trust(10 min)
- Human Agency and Oversight (15 min)
- Technical Robustness and Safety (5 min)
- Privacy and Data Governance (5 min)
- Transparency (15 min)
- Diversity, Non-Discrimination and Fairness (10 min)
- Societal and Environmental Impact
- Accountability

More information

Click here for an overview of all lesson plans of the master human centred AI

Please visit the home page of the consortium HCAIM

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.

The materials of this learning event are available under CC BY-NC-SA 4.0

The HCAIM consortium consists of three excellence centres, three SMEs and four Universities