Fighting Harmful Content with AI: Real-World Safety

Overview

Explore how AI systems can effectively combat harmful online content through scalable moderation techniques, ethical considerations, and innovative approaches to digital safety.

Syllabus

• Introduction to Harmful Online Content
Definition and Types of Harmful Content
Impact of Harmful Content on Society and Individuals
• Fundamentals of AI in Content Moderation
Overview of AI Techniques Used in Moderation
Machine Learning and Natural Language Processing Basics
• Scalable AI Moderation Techniques
Automated Detection and Flagging of Harmful Content
Real-Time Monitoring and Filtering Systems
Case Studies of Scalable Moderation Systems in Practice
• Ethical Considerations in AI-Powered Moderation
Balancing Freedom of Expression and Safety
Privacy Concerns and Data Protection
Bias and Fairness in AI Systems
• Innovative Approaches to Digital Safety
Collaborative AI-Community Models
Use of Reinforcement Learning for Dynamic Adaptation
Advanced Pattern Recognition and Anomaly Detection
• Evaluating AI Moderation Effectiveness
Metrics and Benchmarks for AI Performance
User Feedback and Human Oversight
• Future Trends in AI and Content Moderation
Emerging Technologies and Their Potential Impact
Challenges and Opportunities in Developing Countries
• Practical Workshop: Building an AI Moderator
Hands-On Session with Tools for Content Moderation
Developing a Simple Model to Detect and Flag Content
• Summary and Final Assessment
Recap of Key Concepts
Assignments and Project Presentations
• Additional Resources and Further Reading
Books, Articles, and Research Papers
Recommended Online Courses and Workshops

Subjects

Business