When AI Remembers Too Much
If you work in marketing, sales, or PR and use tools like ChatGPT, Gemini, or Claude regularly, you've probably noticed they "get to know you." This is intentional. Tools like ChatGPT now have memory features that retain details from past chats, like your favorite brands, writing style, or interests. OpenAI even states that the more you use ChatGPT, the more personalized its answers become.
Microsoft's Copilot and Bing Chat also track user history. They can serve answers influenced by your previous searches, chats, and preferences. This personalization can be helpful—like when an assistant nails your brand's voice or suggests your usual topics. But for objective research, it quietly introduces bias.
How This Creates Bias
If you've repeatedly asked AI tools about a specific product, brand, or angle, the AI might start showing you more of the same. It's not malicious—the AI is just trying to be helpful. But this can lead to confirmation bias, where the system amplifies your past interests and subtly filters out different views.
Repeatedly using AI for similar tasks can make it "too agreeable." If you keep prompting it to explain why your product is better than a competitor's, it will. Eventually, it starts assuming your product is always the focus.
Experts call this "bias in use." It's not built into the model itself but emerges from how we interact with it. Over time, the AI begins to mirror your preferences, even when you don't want it to.
Clearing the Slate Isn't Easy
One way to avoid this bias is to clear your context or disable memory. OpenAI, Microsoft, and others offer ways to reset chats, start fresh sessions, or turn off personalization. When memory is off, the model stops tailoring answers based on past chats.
But there's a downside. You lose helpful continuity, like not having to re-explain a project. Without memory, the AI can feel less useful or slower because it no longer "remembers" key details. In a fast-paced workflow, constantly resetting the context is a hassle.
So while clearing memory can reduce bias, it also makes the tool feel less smart. That's a big trade-off for professionals trying to balance speed with objectivity.
A Smarter Fix: Multi-Query Tools
An alternative is to use research tools that don't rely on a persistent chat history at all. Platforms like aureol.ai tackle this problem differently. Instead of one long conversation, they send many smaller, independent queries to AI and online sources, then combine the results.
This approach keeps the AI from leaning too much in one direction. Each query starts fresh, without any bias carried over from your earlier interactions. Our tool even intentionally asks for pros and cons to balance the final output.
Microsoft is already doing something similar with Copilot Search. It gathers information from multiple websites before writing a response, offering more balanced, fact-checked answers. In AI development, this is part of a growing trend: retrieval-augmented generation (RAG), where external documents help ground the AI's answers.
Using multiple prompts and sources also protects against hallucinations or skewed outputs from one-sided queries. It’s like doing research by reading five articles instead of just one blog post
Does the Hypothesis Hold?
The idea that heavy use of generative AI by professionals can create biased outputs is backed by clear evidence. OpenAI, Microsoft, and others confirm their tools personalize answers based on user history. Academic papers and industry blogs also show that users can steer AI responses—sometimes without realizing it—just by how they phrase questions or repeat themes.
Personalization might improve usability, but it makes the results less neutral. In research, where objectivity matters, this can lead to misleading conclusions.
The fix isn't perfect. Resetting memory reduces bias but hurts convenience. Specialized tools that make fresh, API-based queries offer a promising middle ground—reducing user bias while maintaining depth and range.
Generative AI tools adapt to their users—sometimes too well. In fields like marketing and PR, that can quietly tilt research in favor of familiar brands or narratives. While clearing memory can help, it’s not always practical. Instead, tools designed to query multiple sources with a clean context offer a better path to accuracy.
The risk isn't the AI itself; it's how we use it. Staying aware of that can make a big difference.
To learn more check:
OpenAI: Memory and personalization documentationMicrosoft: Copilot and Bing personalization updates