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Introduction
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Classroom Contents
AIUK 2022 Workshop - ExplAIN: AI Explainability in Practice
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- 1 Introduction
- 2 Overview
- 3 Four Principles
- 4 Processbased vs Outcomebased explanation
- 5 Explanation aware design
- 6 Model choice considerations
- 7 Case study
- 8 Motivation
- 9 Case
- 10 Features
- 11 Mural
- 12 Link issues
- 13 Link working
- 14 Sharing the screen
- 15 Requesting video access
- 16 Unconscious biases
- 17 Statistical biases
- 18 Bias mitigation
- 19 Historical bias
- 20 Impact explanation
- 21 Data subject dignity
- 22 Recruitment activity
- 23 Fairness explanation
- 24 AI algorithm
- 25 Legal issues
- 26 Ethical issues
- 27 Problem formulation
- 28 Should we use technology
- 29 Social trust
- 30 Human bias
- 31 Implementation fairness
- 32 Bias
- 33 One last question
- 34 Deep learning models
- 35 Sufficient interpretability
- 36 Outro
