AI Trust and Safety: Navigating the New Frontier
1h 8mIntermediate2025-03-11
Authors
Tiffany Xingyu Wang
Course details
In this pioneering course, acclaimed trust & safety (T&S) leader Tiffany Xingyu Wang tackles a pressing challenge facing organizations: driving AI innovation while staying protected. Bridging debates on ethical, responsible, and safe AI, it provides concrete, T&S-focused guardrails for enterprise AI adoption.
Building on her foundational course Become a Digital Trust and Safety Leader, discover how T&S principles evolve in the AI era. Drawing on vast experience, Tiffany shares practical techniques to mitigate AI risks. Along the way, you’ll explore AI trust frameworks, Safety by Design strategies, risk assessment, red teaming, and regulatory essentials. By the end of this course, you’ll be ready to address emerging challenges so your organization can scale AI while protected.
Whether you’re in T&S, AI engineering, policymaking, or brand management, you’ll gain strategies to embed accountability and oversight across the AI lifecycle.
Learning objectives
Explain the importance of trust and safety in AI systems.
Identify the regulatory frameworks governing AI across different regions.
Outline steps for effective risk mapping throughout the AI lifecycle.
Compile practices for ensuring data quality and mitigating biases in AI training data.
Demonstrate the use of safety evaluations and red teaming in identifying AI vulnerabilities.
Illustrate techniques for integrating explainable AI and privacy-preserving methods.
Describe strategies for user-centric design to enhance AI trust and safety.
Plan a comprehensive incident response framework for AI systems.
Evaluate how to maintain continuous monitoring and feedback loops for AI improvement.
Develop AI governance policies that align with ethical standards and regulatory requirements.
Building on her foundational course Become a Digital Trust and Safety Leader, discover how T&S principles evolve in the AI era. Drawing on vast experience, Tiffany shares practical techniques to mitigate AI risks. Along the way, you’ll explore AI trust frameworks, Safety by Design strategies, risk assessment, red teaming, and regulatory essentials. By the end of this course, you’ll be ready to address emerging challenges so your organization can scale AI while protected.
Whether you’re in T&S, AI engineering, policymaking, or brand management, you’ll gain strategies to embed accountability and oversight across the AI lifecycle.
Learning objectives
Explain the importance of trust and safety in AI systems.
Identify the regulatory frameworks governing AI across different regions.
Outline steps for effective risk mapping throughout the AI lifecycle.
Compile practices for ensuring data quality and mitigating biases in AI training data.
Demonstrate the use of safety evaluations and red teaming in identifying AI vulnerabilities.
Illustrate techniques for integrating explainable AI and privacy-preserving methods.
Describe strategies for user-centric design to enhance AI trust and safety.
Plan a comprehensive incident response framework for AI systems.
Evaluate how to maintain continuous monitoring and feedback loops for AI improvement.
Develop AI governance policies that align with ethical standards and regulatory requirements.
Skills covered
PrivacyResponsible AIData PrivacyCybersecurityArtificial Intelligence (AI)Data ScienceOne-Off
Concepts
0. Introduction
- 01 - Building resilient guardrails for a generative future
1. Foundations of AI Trust and Safety
- 02 - Historical context - From content moderation to AI governance
- 03 - Why AI trust and safety matters
- 04 - Core principles - SAFER framework
- 05 - Stakeholders and roles in AI trust and safety
2. The GenAI Era
- 06 - GenAI basics
- 07 - GenAI risks and challenges
- 08 - Learning from major GenAI platforms
3. AI Safety Risk Assessment and Mapping
- 09 - Purpose and steps of risk mapping
- 10 - Motivations - Why attackers target AI
- 11 - Abuse vectors across the AI lifecycle
- 12 - Emerging exploits and next-generation tactics
4. Regulatory and Governance Essentials
- 13 - Global regulatory landscape
- 14 - Corporate governance and internal policy
- 15 - Documentation and reporting - Building transparency and trust
5. Technical Tools and Design Strategies
- 16 - The importance of high-quality training data
- 17 - Techniques for safety evaluations and red teaming
- 18 - User-centric design for trust and safety
- 19 - Integrating explainable AI and privacy techniques
6. Ongoing Monitoring and Mitigation
- 20 - Real-time monitoring for AI systems
- 21 - Developing feedback loops for continuous improvement
- 22 - Incident response and management for AI systems
7. Strategic Outlook
- 23 - Future directions and enterprise adoption
- 24 - Agentic AI and oversight
- 25 - Differentiating through trust
- 26 - Scaling across the enterprise
- 27 - Thought leadership in AI trust and safety
- 28 - Navigating global regulatory trends
Conclusion
- 29 - Championing AI trust and safety