Responsible AI and Ethics
Description
Comprehensive understanding of responsible AI principles, ethical considerations, bias mitigation, and frameworks for developing and deploying AI systems responsibly.
Learning Objectives
Learners will understand Google's AI principles and responsible AI frameworks, identify and mitigate bias in AI systems, implement ethical AI practices, understand privacy and security considerations, and develop governance frameworks for responsible AI deployment in organizations.
Topics (7)
Comprehensive overview of Google's seven AI principles including being socially beneficial, avoiding unfair bias, being built and tested for safety, and being accountable to people.
Understanding different forms of bias in AI systems including algorithmic bias, data bias, and representation bias, along with tools and techniques for detection and mitigation.
Understanding privacy regulations, data minimization principles, differential privacy, federated learning, and other privacy-preserving AI techniques.
Understanding regulatory landscapes, governance structures, compliance requirements, and frameworks for responsible AI deployment in organizations.
Methods for making AI systems more interpretable including attention visualization, feature importance analysis, and explanation generation techniques.
Strategies for stakeholder engagement, conducting AI impact assessments, gathering community feedback, and ensuring inclusive AI development processes.
Comprehensive coverage of AI safety risks including adversarial attacks, model poisoning, prompt injection, and defensive strategies for securing AI systems.