“Level Setting” – The Fundamentals of AI, Algorithmic Decision-Making, Testing and How They All Work: The Essentials of ChatGPT, Bard and More Tools for Non-IT Professionals
CEO & Founder
BABL AI Inc.
Workshops are offered In-Person only
Let’s level set in our morning workshop. Join us as experts provide a complete roadmap to understanding the universe of AI stakeholders and capabilities, as well as the legal, compliance, safety, ethics, and more issues to flag now. By the end of this workshop, you will gain an actionable blueprint that will lay the groundwork for your work after the conference-and for the main conference discussions over the next two days. Along with speaker -prepared reference materials, gain updates and best practices on key topics, including:
- A roadmap to the lengths and limits of AI tools-and their associated risks and benefits
- Demystifying and clarifying key concepts, including bias, responsible use, safety, hallucinations, deep learning and more
- What is (and isn’t) AI and algorithmic decision-making vs. machine learning and data analytics
- What is Natural Language Processing (NLP) and what is its role in the AI ecosystem
- Identifying key compliance and ethics concerns with NLP systems
- How to determine if your existing compliance framework can be evolve to keep up with the rapid pace of AI: Special considerations for Automated decision-making and more capabilities
- Defining “Responsible Use”
- Understanding the functionalities of ChatGPT, Bard and similar tools
- What’s responsible vs. not
- Creating internal versions of ChatGPT
- To what extent employees can use ChatGPT, Bard and similar tools
- What types of policies should be in place?
- Laying the groundwork for addressing privacy issues, including the protection of sensitive data and client information
- When do you inadvertently waive privilege by leveraging tolls?
- Key regulations and compliance frameworks with respect to AI
- When to seek legal advice around algorithmic decision-making plus testing automated decision-making systems for bias, discrimination, bias, privacy