UX Researcher @ Perceptual User Experience Lab | Human-AI Interaction Researcher @ University of Arkansas at Little Rock
AI systems don’t just reflect the world as it is - they reinforce the world as it's been. When the values baked into those systems are misaligned with the needs and expectations of users, the result isn’t just friction. It’s harm: biased decisions, opaque reasoning, and experiences that erode trust. UX researchers are on the front lines of this problem. Every time we study how someone interacts with a model, interprets its output, or changes behavior based on an algorithmic suggestion, we’re touching alignment work - whether we call it that or not. Most of the time, this problem doesn’t look like sci-fi. It looks like users getting contradictory answers, not knowing why a decision was made, or being nudged toward actions that don’t reflect their intent. It looks like a chatbot that responds confidently but wrongly. Or a recommender system that spirals into unhealthy loops. And while engineers focus on model architecture or loss functions, UX researchers can focus on what happens in the real world: how users experience, interpret, and adapt to AI. We can start by noticing when the model’s behavior clashes with human expectations. Is the system optimizing for the right thing? Are the objectives actually helpful from a user’s point of view? If not, we can bring evidence - qualitative and quantitative - that something’s off. That might mean surfacing hidden tradeoffs, like when a system prioritizes engagement over well-being, or efficiency over transparency. Interpretability is also a UX challenge. Opaque AI decisions can’t be debugged by users. Use methods that support explainability. Techniques like SHAP, LIME, and counterfactual examples can help trace how decisions are made. But that’s just the technical side. UX researchers should test whether these explanations feel clear, sufficient, or trustworthy to real users. Include interpretability in usability testing, not just model evaluation. Transparency without understanding is just noise. Likewise, fairness isn’t just a statistical property. We can run stratified analyses on how different demographic groups experience an AI system: are there discrepancies in satisfaction, error rates, or task success? If so, UX researchers can dig deeper into why - and co-design solutions with affected users. There’s no one method that solves alignment, but we already have a lot of tools that help: cognitive walkthroughs with fairness in mind, longitudinal interviews that surface shifting mental models, participatory methods that give users a voice in shaping how systems behave. If you’re doing UX research on AI products, you’re already part of this conversation. The key is to frame our work not just as “understanding users,” but as shaping how systems treat people. Alignment isn’t someone else’s job - it’s ours too.