Staying Safe in the AI Future

Overview

Delve into the crucial strategies for ensuring a safe AI future with our insightful event, "Staying Safe in the AI Future." This session will illuminate paths to develop and use artificial intelligence responsibly, highlighting key issues such as maintaining data quality, mitigating biases, and adhering to ethical standards. By addressing these vital aspects, we aim to foster an AI-driven future that prioritizes safety and responsibility.

Hosted on YouTube, this event is a must-attend for anyone interested in the sustainable development of AI technologies.

Whether you're an AI professional, a researcher, or simply an enthusiast, you'll gain valuable insights from leading experts in the field.

Categories covered include Artificial Intelligence Courses and Conference Talks, making this an excellent opportunity for learning and growth in the AI domain. Ensure you stay at the forefront of responsible AI innovation by participating in this important discussion.

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