Contemporary organizations invest in user experience research (UXR) to derive objective insights that truly reflect user needs, preferences, and behaviors. These insights are critical inputs to product design strategies, ensuring that products and services meet user needs and deliver value to the organization. Although the duties of a UX researcher may vary based on organizational needs, in general, UX researchers collaborate with cross-functional teams to design and execute research, collect and analyze research data, and share and present research insights with stakeholders to inform decision-making.
However, UX researchers often rely on their previous training, experiences, and background, which can be influenced by preformed stereotypes, prejudices, and mental constructs. These biases can impact decision-making and hinder the ability to deliver objective insights [1]. Cognitive biases such as confirmation bias, anchoring, and the halo effect can skew research findings and prevent valuable insights from being delivered. Consequently, products and services developed with skewed insights often have a narrow focus, prioritize the wrong problems, and neglect the needs and perspectives of diverse users, delivering little to no value to organizations. The implications of cognitive biases are profound as they can steer product development in misaligned directions that fail to address the actual needs or problems of users.
1. Current Strategies and Limitations
Recognizing the negative impact of cognitive biases, the UX research community has proposed various strategies to mitigate them. Examples include triangulation [2], awareness and education [3], and adhering to standard research procedures [4]. While these strategies have helped, various limitations persist. Some are heuristic in nature, highly subjective, and often impractical or expensive to implement. For instance, some organizations may not be willing to invest funds to educate UX researchers and create the necessary awareness to eliminate cognitive bias. Moreover, awareness of cognitive bias does not guarantee mitigation.
2. The Role of Generative AI
Generative AI (GAI) tools like ChatGPT, Midjourney, and Gemini, see Figure 1, offer promising possibilities that may address most limitations of current strategies for mitigating bias and help deliver research insights that justify investment. GAI tools are products of complex technologies, algorithms, and statistical techniques. They are trained with big datasets and optimized to perform natural language processing (NLP) tasks such as sentence segmentation, keyword extraction, and sentiment analysis. With a high level of accuracy difficult to achieve by a UX researcher, GAI tools can analyze large datasets to identify inconsistencies, patterns, and keywords that indicate potential biases, flagging these for further review by UX researchers.