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Overview
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Classroom Contents
Responsible AI for Engineers
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- 1 Intro
- 2 Rons background
- 3 Overview
- 4 Imagenet
- 5 Google Search
- 6 Diabetic Retinopathy
- 7 Google Assistant
- 8 Google Cloud
- 9 Problems with AI
- 10 disproportionate performance
- 11 Compass
- 12 Microsoft Twitter
- 13 Filter Bubbles
- 14 Amazon Search Algorithm
- 15 Principles
- 16 Digital Wellbeing
- 17 Machine Learning AI
- 18 Data Cards
- 19 Model Cards
- 20 Model Interpretation
- 21 Local Interpretation
- 22 Shapley Values
- 23 Integrated gradients
- 24 Gradients
- 25 Examples
- 26 Visualization Tools
- 27 Multiple metrics
- 28 Secondary models
- 29 Over optimizing
- 30 Case study fairness
- 31 How does prediction change
- 32 How effective is this system
- 33 What if we generalize
- 34 Collecting more data
- 35 Changing the loss function
- 36 The net effect
- 37 Lessons
- 38 AI Guidebook
- 39 AI Ethics Guidelines
