Staying Safe in the AI Future

Overview

Explore strategies for responsible AI development and usage, focusing on data quality, bias mitigation, and ethical considerations to ensure a safer AI-driven future.

Syllabus

• Introduction to AI and Its Impact
Overview of AI technologies
Historical context and evolution
Current applications and future trends
• Data Quality in AI
Importance of high-quality data
Data collection methods and challenges
Cleaning and preprocessing data
Case studies highlighting the impact of data quality
• Bias Mitigation in AI Systems
Understanding bias in AI
Sources and types of biases
Techniques for identifying and reducing bias
Implementing fairness-aware algorithms
• Ethical Considerations in AI Development
Principles of ethical AI
Privacy and security concerns
Ethical dilemmas and decision-making frameworks
Case studies of ethical AI failures and successes
• Legal and Regulatory Frameworks
Overview of AI regulations globally
Compliance and governance in AI development
The role of policy in responsible AI
• AI in Society: Impacts and Responsibilities
AI's influence on labor and economy
Social implications and digital divide
Designing inclusive AI systems
• Strategies for Responsible AI Development
Stakeholder involvement and interdisciplinary collaboration
Tools and methodologies for responsible AI
Monitoring and evaluation of AI systems
• Ensuring a Safer AI-driven Future
Risk management and mitigation strategies
Long-term considerations and sustainability
Community engagement and public awareness
• Course Review and Capstone Project
Review of key concepts
Capstone project: Developing a strategy for a responsible AI application
Feedback and course reflection

Subjects

Conference Talks