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Starts 7 June 2025 01:52
Ends 7 June 2025
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Overview
Explore key challenges in AI trust development, from benchmark construction and evaluation methods to data quality issues and human trust factors in artificial intelligence systems.
Syllabus
- Introduction to Trust in AI
- Benchmark Construction for AI Trust
- Evaluation Methods for Trustworthiness
- Data Quality Issues
- Human Factors and Trust in AI
- Balancing Trust and Distrust
- Case Studies and Real-World Applications
- Future Directions in AI Trust Research
Definition and Importance of Trust in AI
Historical Context and Evolution of Trust in AI
Criteria for Reliable Benchmarks
Methods for Benchmark Construction
Common Issues in Current Benchmarks
Quantitative vs. Qualitative Evaluation
Tools and Techniques for Evaluation
Case Studies of Effective Evaluation Methods
Impact of Data Quality on Trust
Data Biases and Their Mitigation
Best Practices for Ensuring Data Integrity
Psychological Aspects of Trust
User-Centric Design for Trustworthy Systems
Communicating AI Decisions to Users
Scenarios of Overtrust and Undertrust
Designing for Appropriate Levels of Trust
Regulatory and Ethical Considerations
Analysis of Successful Trust-Building in AI
Lessons Learned from High-Profile Failures
Emerging Challenges and Opportunities
The Role of Interdisciplinary Approaches
Forecasting the Next Decade of Trust in AI
Subjects
Data Science
