Measuring User Experience Inclusivity in Human-AI Interaction via Five User Problem-Solving Styles

Motivations: Recent research has emerged on generally how to improve AI product user experiences, but relatively little is known about an AI product's inclusivity. For example, what kinds of users does it support well, and who does it leave out? And what changes in the product would make it more inclusive? Objectives: Our overall objective is to help fill this gap, investigating what kinds of diverse users an AI product leaves out, and how to act upon that knowledge. To bring actionability to our findings, we focus on users' diversity of problem-solving attributes. Thus, our specific objectives were: (1) to reveal whether participants with diverse problem-solving styles were left behind in a set of AI products; and (2) to relate participants' problem-solving diversity to their demographic diversity, specifically, gender and age. Methods: We performed 18 experiments, discarding two that failed manipulation checks. Each experiment was a 2x2 factorial experiment with online participants. Each experiment compared two AI products: one deliberately violating an HAI guideline and the other applying the guideline. For our first objective, we analyzed how much each AI product gained/lost inclusivity compared to its counterpart, where inclusivity was supportiveness to participants with particular problem-solving styles. For our second objective, we analyzed how participants' problem-solving styles aligned with their demographics, namely their genders and ages. Results Implications: Participants' diverse problem-solving styles revealed six types of inclusivity results: (1) the AI products that followed an HAI guideline were almost always more inclusive across diversity of problem-solving styles than the products that did not follow that guideline-but the "who" that got most of the inclusivity varied widely by guideline and by problem-solving style...