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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