AI & UXR, CHAT GPT
AI, Bias and the Power of Questions: How to Get Better Answers With Smart Prompts
3
MIN
Jul 17, 2025
How objective are AI answers really?
The sobering truth: Not at all. Every answer is based on a prompt – and every prompt already carries a perspective. This is where the problem begins. But also the solution.
In our new blog article (and white paper), we show that to use AI effectively, you need to be able to ask questions – smart, conscious, bias-sensitive questions. Or, in short: a good prompt is the best bias filter.
Why bias cannot simply be “trained away”
Bias in AI does not arise at the output stage, but deep down inside: through the training data, through the model's objective – and, last but not least, through the questions we ask. This shows time and again that:
👉 AI does not reflect the world – it condenses probabilities.
Whether you work in UX research, communication or strategy, if you only ask plausible questions, you will usually get a very pleasing but often too smooth answer. The result? Reproduction instead of reflection.
A systematic look at bias: 13 types, many pitfalls
In the white paper, you will find a tabular overview of 13 types of bias – from training data bias to prompt bias to persona bias. For each type, you will find:
a clear description,
a practical example,
an avoidance strategy
and a specific prompt for application.
Here's a little preview:
🧠 Prompt bias
Description: The wording of the question itself suggests a particular direction.
Example: ‘Why doesn't gender mainstreaming work?’
Better prompt: ‘Please check my question for hidden assumptions.’
🌍 Representation Bias
Description: Groups are portrayed in a stereotypical manner or not at all.
Example: Trans issues are only addressed from a medical or legal perspective.
Better prompt: ‘What do queer or neurodivergent people have to say about this?’
🗓️ Temporal bias
Description: AI knows nothing about current developments.
Example: The legal situation regarding trans rights is from 2022.
Better prompt: ‘Please state your knowledge date – what could have happened since then?’
Your cheat sheet: 7 strategies for bias-sensitive prompting
Here are seven universally applicable strategies you can use to make AI responses more informed, inclusive and reflective:
Use neutral wording:
→ ‘How well is UX typically anchored in companies – and what could be the reasons for this?’
Demand diversity of perspectives:
→ ‘Please give me three perspectives: conservative, progressive and activist.’
Actively seek opposing opinions:
→ ‘What are the arguments against my thesis – even if it sounds plausible?’
Be aware of cultural differences:
→ ‘How would this be viewed in Germany?’
Encourage self-reflection:
→ ‘What implicit assumptions does my question contain?’
Set roles consciously:
→ ‘Answer as an investigative journalist.’
Address bias directly:
→ ‘What biases could arise in this topic – at the level of data, language, perspectives and goals?’
Three application scenarios – and suitable prompts
We have selected three typical use cases and formulated suitable prompt templates:
🔍 Interview guidelines (UX/research/transformation)
Objective: Identify leading questions and bias
👉 Prompt:
"Please review the following interview guide for potential bias risks (e.g., implicit judgements, cultural bias)."
💬 Corporate communication / LinkedIn / Thought leadership
Objective: Reflect on narratives instead of reproducing them
👉 Prompt:
"Are there dominant narratives or missing voices? Which groups or perspectives are being neglected?"
📈 Strategy / Policy / Management Consulting
Goal: Think from multiple perspectives
👉 Prompt:
"What implicit assumptions underlie the plan – and who benefits (or doesn't benefit)?"
Conclusion: Better questions lead to smarter AI
There is no such thing as perfect neutrality – but there are better questions.
Bias in AI cannot be eliminated, but its impact can be significantly reduced with good prompting. The ability to not only ask questions, but to ask them consciously, is key.
Because every good answer starts with a good question. And that question comes from you.
💌 Not enough? Then read on – in our newsletter. It comes four times a year. Sticks in your mind longer. To subscribe: https://www.uintent.com/newsletter
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AUTHOR
Tara Bosenick
Tara has been active as a UX specialist since 1999 and has helped to establish and shape the industry in Germany on the agency side. She specialises in the development of new UX methods, the quantification of UX and the introduction of UX in companies.
At the same time, she has always been interested in developing a corporate culture in her companies that is as ‘cool’ as possible, in which fun, performance, team spirit and customer success are interlinked. She has therefore been supporting managers and companies on the path to more New Work / agility and a better employee experience for several years.
She is one of the leading voices in the UX, CX and Employee Experience industry.
