Responsible AI: Principles and Practices for Ethical Machine Learning

Overview

Explore AI's impact on business strategy and innovation with insights from industry expert Kathryn Hume at TMLS2019.

Syllabus

• Introduction to Responsible AI
Overview of AI's impact on business and society
Importance of ethics in AI development
• Fundamental Principles of Ethical AI
Fairness and Bias in AI systems
Transparency and Explainability in AI
Accountability in AI development and deployment
• Building Ethical AI Applications
Strategies for reducing bias in data and algorithms
Techniques for improving model interpretability
Methods to ensure accountability and compliance
• Case Studies in Ethical AI Deployment
Real-world examples of ethical AI challenges
Lessons learned from industry experiences
• AI Governance and Regulation
Overview of existing AI policies and regulations
Best practices for implementing ethical guidelines in organizations
• Tools and Frameworks for Responsible AI
Introduction to AI fairness and auditing tools
Frameworks for ethical decision-making in AI
• The Role of Business in Promoting Ethical AI
Ethical considerations in AI-driven business strategies
Balancing innovation and responsibility
• Workshop: Designing an Ethical AI Strategy
Hands-on exercises in ethical AI design
Group discussions on potential ethical dilemmas
• Future Trends in Ethical AI
Emerging technologies and their ethical implications
Preparing for the future of responsible AI development
• Conclusion and Final Reflections
Recap of key principles and practices in ethical AI
Strategies for ongoing ethical vigilance in AI projects

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Subjects

Data Science