Introduction
Imagine you’re about to use a new AI system that screens job applications at your company. The vendor claims it’s “fair and non-discriminatory”.
How can you be sure that the vendor’s claim is true? What reasoning has been provided to assure you that it won’t discriminate against certain groups? What evidence would convince you?
In this module, you’ll explore an approach to answering these questions known as Trustworthy and Ethical Assurance (or TEA for short). But rather than starting with definitions and theory, we’re going to jump straight into a practical example.
Learning Objectives
By the end of this module, you will be able to:
Identify the key components of an assurance case
Recognise goals, strategies, property claims, evidence, and context elements
Explain how structured arguments build trustworthiness
Understand the logical flow from to-level goal to evidence
Trace argument chains in a realistic assurance case
Follow strategies and connections in an interactive and illustrative example of a Fair Recruitment AI case
Evaluate the strength and completeness of assurance arguments
Critically assess what makes arguments convincing and identify gaps