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Motivations: Recent research has emerged on generally how to improve AIproduct user experiences, but relatively little is known about an AI product'sinclusivity. For example, what kinds of users does it support well, and whodoes it leave out? And what changes in the product would make it moreinclusive?  Objectives: Our overall objective is to help fill this gap, investigatingwhat kinds of diverse users an AI product leaves out, and how to act upon thatknowledge. To bring actionability to our findings, we focus on users' diversityof problem-solving attributes. Thus, our specific objectives were: (1) toreveal whether participants with diverse problem-solving styles were leftbehind in a set of AI products; and (2) to relate participants' problem-solvingdiversity to their demographic diversity, specifically, gender and age.  Methods: We performed 18 experiments, discarding two that failed manipulationchecks. Each experiment was a 2x2 factorial experiment with onlineparticipants. Each experiment compared two AI products: one deliberatelyviolating an HAI guideline and the other applying the guideline. For our firstobjective, we analyzed how much each AI product gained/lost inclusivitycompared to its counterpart, where inclusivity was supportiveness toparticipants with particular problem-solving styles. For our second objective,we analyzed how participants' problem-solving styles aligned with theirdemographics, namely their genders and ages.  Results & Implications: Participants' diverse problem-solving styles revealedsix types of inclusivity results: (1) the AI products that followed an HAIguideline were almost always more inclusive across diversity of problem-solvingstyles than the products that did not follow that guideline-but the "who" thatgot most of the inclusivity varied widely by guideline and by problem-solvingstyle...

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