3 ways to reduce bias in AI with better context | MarTech

"Context is what we give to our AI model to help it sort, analyze and report results and insights accurately. It's like adding conditions when you're building an automated email workflow. This goes beyond the basic questions about which model to use and what to use it for. We have to remember that we have an incredibly powerful tool, but it's not foolproof."

"AI does know a lot, but only you know the context in which you're asking questions. In short, AI can't read our minds. All too often, we build queries that assume it does. That colors the answers AI gives us."

"The bias can stem from not providing context and nuance - the knowledge that lives in our heads, which we call on when we make decisions on our own but forget to consider when working with AI."

Marketers often overlook a critical concern when integrating AI into decision-making: the assumption that AI understands the context and nuance that exists in human minds. This oversight introduces bias into AI models and queries. Context is essential because it provides AI with the conditions and information needed to sort, analyze, and report results accurately. Without proper context, AI cannot access the implicit knowledge marketers rely on for their own decisions. While AI possesses significant capabilities, it cannot read minds or infer unstated assumptions. Marketers must consciously provide detailed context, nuance, and conditions when building queries to ensure AI delivers accurate and useful insights rather than distorted answers based on incomplete information.

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