5 Common Biases in User Interviews (And How AI Solves Them)
Humans are biased creatures. Even the best researchers fall into traps like confirmation bias. Here is how AI interviewers can help reduce the noise.
The observer effect states that the act of observing a phenomenon changes it. In user research, this is painfully true. When a Product Manager interviews a user, the data is polluted by the human dynamic.
Here are 5 common biases and how AI helps mitigate them.
1. Social Desirability Bias
The Bias: Users want to look good. They want to be helpful. If you ask, "Would you use this feature to exercise more?", they say "Yes!" because exercising is good. The AI Fix: Users often feel less judged by a bot. They are more likely to admit, "Honestly, I'm lazy and I won't use it." AI provides a non-judgmental space for raw honesty.
2. Confirmation Bias
The Bias: You unknowingly dig deeper when a user says what you want to hear, and move on quickly when they disagree with your hypothesis. The AI Fix: Fieldrun's AI agents follow a strict protocol. They probe equally on positive and negative feedback, ensuring you don't just cherry-pick the wins.
3. The "Nice" Factor
The Bias: Users don't want to hurt your feelings. If you say "I built this," they will say "It's great." The AI Fix: An AI interviewer has no feelings to hurt. You can frame the AI as a third party: "I'm a research assistant collecting honest feedback for the team. You won't hurt my feelings."
4. Leading Questions
The Bias: "How much do you love this new design?" (Implies they should love it). The AI Fix: AI prompts are pre-scripted and reviewed to be neutral. "What is your reaction to this design?" consistently yields cleaner data.
5. Interviewer Fatigue
The Bias: By the 10th interview of the day, you are tired. You miss cues. You forget to ask the follow-up. The AI Fix: AI has infinite energy. The 1,000th interview is conducted with the same curiosity and precision as the first one.
Conclusion
Human empathy is irreplaceable for deep emotional discovery. But for validating features and gathering honest feedback at scale, the neutrality of AI is a feature, not a bug.