Главная
Study mode:
on
1
Introduction
2
What is the Pear Guide Book
3
Chapter Updates
4
Case Studies
5
Workshop Resources
6
User Need
7
Patterns
8
Early good data practices
9
Embrace noisy data
10
Maintain the data set
11
Create realistic data
12
Recommendations course
13
Recommendation Systems course
14
User Experience
15
Design Patterns
16
Setting the right expectations
17
Explaining the benefits
18
Explaining recommendations
19
Recap
20
Model Confidence
21
Errors
22
Examples of errors
23
Let users give feedback
24
What action will be taken
25
Example
26
Summary
Description:
Gain insights from the People + AI Research team on building trustworthy, user-centered AI products in this 55-minute workshop. Explore opportunities in the AI development process for improving and calibrating user trust through a series of exercises. Learn about a broader toolkit of resources for further exploration, including the People + AI Guidebook and techniques for classifying text with BERT. Discover best practices for early data handling, maintaining datasets, creating realistic data, and designing user experiences that set appropriate expectations. Understand how to explain AI benefits and recommendations, handle model confidence and errors, and incorporate user feedback. Walk away with practical knowledge to develop AI products that balance innovation with user trust and ethical considerations.

Building Trusted AI Products - Workshop

TensorFlow
Add to list
0:00 / 0:00