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1
Introduction
2
What is Responsible AI
3
Potential for Harm
4
AI Risks
5
Agenda
6
EU AI Act
7
Humancentered Design
8
Error Prevention Principle
9
Humancentered Design Development
10
This is Reality Argument
11
Math Argument
12
Bias Argument
13
Humancentered AI
14
Cultural Organizational Technical Challenges
15
New Tools and Processes
16
Recap
17
Measurements
18
Translation Failures
19
Common Metrics
20
Information Retrieval
21
Overreliance
22
Search
23
Conclusion
24
Questions
25
Data collection
26
Question from the audience
27
Conflicting values
28
Explicit values
29
Literacy
30
Responsibility
31
Empower Users
32
Search Metrics
33
Metrics in Responsible AI
34
Measuring and Predicting Mistakes
35
Predicting Mistakes
36
Outro
Description:
Explore the challenges and opportunities in responsible AI development through this thought-provoking ACM conference talk. Delve into the pressing need for a human-centered approach in addressing AI risks and failures. Examine the cultural shift required to move the industry from a focus on model performance to holistic AI system development. Discover the tooling advances necessary to empower practitioners in dealing with AI's probabilistic and adaptive capabilities. Draw connections between responsible AI challenges and familiar problems in search and information retrieval. Gain insights into operationalizing human-centered AI, ensuring benefits for people and society, and incorporating human considerations throughout the development process. Learn about the EU AI Act, error prevention principles, and the importance of measurements and metrics in responsible AI practices.

Challenges and Opportunities in Responsible AI

Association for Computing Machinery (ACM)
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