As AI is increasingly incorporated into their operations, businesses need to understand the underlying technologies and data flows in order to facilitate model integration, measure for bias, and promote fairness. Not all businesses can accurately define when they are using artificial intelligence, or be reliably confident in their knowledge of the internal data flows powering these systems. In addition, organizations responsible for AI models may not be familiar with all sources of data, or have identifiable tracking, documentation, and oversight of their outputs.
The Fundamentals of AI Governance session will cover topics including:
- The key markers of artificial intelligence, machine learning, and data analytics in existing systems;
- The essentials of data flows, data management, and data use for production level artificial intelligence applications;
- The basics of AI models, managing and testing models, and deploying AI; and
- The importance of understanding data and models as two levers to pull when managing bias and fairness in artificial intelligence and machine learning models.