Generative AI: Ethics, Black Boxes, Explainability

Generative AI: Ethics, Black Boxes, Explainability

This second session in our CAI Learning Series focuses on Generative AI as a strategic imperative. AI is at the top of the hype cycle and it’s not coming down. How do you cut through the noise to gain a full understanding of the impact on your organization? In this session, CAI welcomes Rainbird, a cognitive decision automation platform for scaling knowledge and automating human decision-making. Join us for a discussion on ethics, the limitations of black boxes, and explainability as it relates to large language models (LLM) and ChatGPT.

Audience learning objectives and key takeaways:

• Cut through the noise for a full understanding of the implications of adding generative AI and LLM to your organization.
• Learn how to add predictability, consistency, and auditability to your AI solutions.
• Understand what explainability is expected to achieve and how it differs related to degrees of technical expertise, understanding, and fear.
• The need to consider ethics, policy, and governance around AI models, then proceed with caution in a strategy for success.

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