🚀 Bias, fairness, and explainability

Online course module 2/5 from building safe intelligence systems

🚀 Bias, fairness, and explainability | Online course module 2/5 from building safe intelligence systems

Bias, fairness, and explainability (online course)

Hello,

The "Building safe intelligence systems" delves into the critical aspects of AI safety and security, providing you with the knowledge and tools to navigate this rapidly evolving landscape. We explore:

• The ethical foundations of AI: Understanding the values and principles that should guide AI development and deployment.

• Mitigating AI threats: Identifying and addressing potential biases, vulnerabilities, and unintended consequences.

• Building robust and resilient AI systems: Developing secure AI architectures and implementing safeguards against adversarial attacks.

• Fostering collaboration and transparency: Creating open and inclusive AI governance and knowledge sharing processes.

Read more on the why and how:

🚀 Building safe intelligence systems: moving beyond the AI dystopia (new online course)

This syllabus provides a comprehensive framework for the "Building safe intelligence systems" series.

The extracted parts are focused on understanding the approach to adopt before the full course is released.

Each module's specific content and format will be further detailed, including practical/technical aspects, recommendations, and real-world applications exploration to meet the needs and interests of each one:

Module 1: Foundations of AI safety (last week)

Module 2: Bias, fairness, and explainability (this week)

Module 3: Security and robustness

Module 4: Human-AI collaboration and governance

Module 5: Emerging challenges and future trends