Responsible Generative AI - How To Avoid Gen AI Risks?
Date: Thursday, November 21 2024
Time: 1:00 PM (EST) / 10:00 AM (PST)
Location: Virtual
Duration: 1 hour
Register Here
First Step is to Register so we can follow up with you for more details.
Responsible Generative AI - How To Avoid Gen AI Risks?
Date: Thursday, November 21 2024
Time: 1:00 PM (EST) / 10:00 AM (PST)
Location: Virtual
Duration: 1 hour
Register Here
First Step is to Register so we can follow up with you for more details.
About the workshop
We are excited to invite you to an exclusive business solution session designed to delve into the critical aspects of Responsible AI, Ethical AI, and AI Governance. This interactive session will provide you with the knowledge and tools to navigate the complex landscape of AI development and deployment, ensuring your AI projects are not only innovative but also ethical and responsible.
Agenda
Emerging Risks and Challenges with Generative AI
- Understand the current and potential risks associated with Generative AI
- Explore real-world case studies highlighting challenges in Generative AI
Pillars of Responsible AI
- Define the core principles and pillars of Responsible AI
- Analyze the importance of fairness, transparency, accountability, and privacy in AI systems
- Responsible AI practices at production grade
Responsible AI - Adoption and Improvements
- Strategies for adopting Responsible AI within an organization
- Evaluate the continuous improvement processes for maintaining Responsible AI
Integrating Technology with Responsible AI
- Examine the role of technology in supporting Responsible AI practices
- Tools and technologies that can aid in developing and monitoring Responsible AI
- Identify integration points for Responsible AI within existing technology stacks
Responsible AI - Central Observability & Governance
- Understand the importance of central observability in Responsible AI
- Learn about governance frameworks and their application in AI projects
- Develop strategies for monitoring and governing AI systems effectively & efficiently
How to Achieve Risk Coverage at Each Technology Layer
- Identify different technology layers involved in AI deployment
- Discuss methods for achieving comprehensive risk coverage at each layer
- Develop a risk management plan tailored to AI technologies
Demo
- PTG Demo of Responsible AI POC's
- Real-world application of Responsible AI principles
- Encourage interactive participation to reinforce learning through hands-on experience
Common use cases
- Fraud detection and prevention in Financial Services
- Fairness and non-discrimination in AI Recruiting Tools
- Beneficence and non-maleficence in AI Medical Diagnosis Transparency and fairness in AI content moderation
Takeaways
- In-depth Understanding: A thorough understanding of the principles of responsible AI, ethical AI, and AI governance, including key concepts like fairness, accountability, transparency, and reliability.
- Practical Knowledge: Practical knowledge of best practices for implementing responsible and ethical AI in various projects, ensuring that AI systems are fair, unbiased, and transparent.
- Case Studies and Use Cases: Insights from real-world case studies and use cases demonstrating the application and importance of responsible AI, ethical AI, and effective AI governance frameworks.
- Strategies for Implementation: Strategies and tools for integrating ethical considerations, responsibility, and governance throughout the AI project lifecycle.
- Problem-Solving Skills: Enhanced problem-solving skills to address common ethical challenges in AI development and deployment.
- Governance Frameworks: An understanding of AI governance frameworks and policies that promote ethical and responsible AI practices within organizations.
- Actionable Steps: Clear, actionable steps to foster a culture of ethics, responsibility, and governance within AI teams and organizations.
Ideal for
- CIO, CTO, CPO, CDO, CISO, VP of Engineering, Enterprise Architect
- Director or Engineering Manager (Software)
- Director or Manager of Data Engineering (or Sr Data Engineer)
- Director or Manager of Data Science/Analytics (or Sr Data Scientist)
Participants will be better equipped to develop and manage AI systems that are not only technically proficient but also ethically aligned and socially responsible, thereby contributing positively to their organizations.
November 21, 2024
1:00PM (EST) / 10:00AM (PST)
Virtual
