Large language models often produce biased or stereotypical outputs. One way to reduce this possibility is to be more inclusive in our prompts, but doing so may not come naturally to most users. Therefore, we designed a tool that coaches users to write more inclusive prompts—a strategy that leverages design friction to provide a media literacy intervention. Data from a user study (N=344) show that compared to no coaching, inclusive prompt coaching directly increased users’ awareness of algorithmic bias and their perceived prompting efficacy. It also indirectly enhanced their trust in the system and perceived trust calibration through cognitive elaboration. However, inclusive prompt coaching resulted in a less satisfying user experience. These findings have implications for ethical interventions in prompting for better communicating and combating algorithmic bias. We discuss the benefits and limitations of inclusive prompt coaching, as well as ways to balance usability for long-term adoption of generative AI systems.

The evolution of information-seeking processes, driven by search engines like Google, has reshaped how people access and interact with information. This paper examines how individuals' pre-existing attitudes on polarizing topics, such as the legality of abortion, influence their engagement with modern information-seeking processes. Recruiting participants from an undergraduate population of a university we use a mixed-methods study involving surveys and information-seeking tasks focused on the legality of abortion, this work offers five key insights. First, individuals with opposing abortion-related attitudes receive different search results. Second, the vocabulary used when formulated search queries differs significantly across opposing attitudes. Third, this difference in query vocabulary has a significant effect on the search results. Fourth, this effect remains consistent, though reduced, when personalization is removed from the process. Finally, Google Search returns search results that align with users' pre-existing attitudes, thereby reinforcing those attitudes. Taken together, these findings reveal a critical relationship between human biases, partisan audiences, and algorithmic processes. Specifically, our findings underscore how search platforms such as Google Search, which play a crucial role in the modern information-seeking process, contribute to information polarization.

Abstract Online health communities (OHCs) have been major resources for people with similar health concerns to interact with each other. They offer easily accessible platforms for users to seek, receive, and provide supports by posting. Taking the advantage of text mining and machine learning techniques, we identified social support type(s) in each post and a new user's support needs in an OHC. We examined a user's first‐time support‐seeking experience by measuring both quantity and quality of received support. Our results revealed that the amount and match of received support are positive and significant predictors of new users' continued engagement. Our outcomes can provide insight for designing and managing a sustainable OHC by retaining users.

There is a growing concern about the extent to which algorithmic personalization limits people's exposure to diverse viewpoints, thereby creating “filter bubbles” or “echo chambers.” Prior research on web search personalization has mainly reported location-based personalization of search results. In this paper, we investigate whether web search results are personalized based on a user's browsing history, which can be inferred by search engines via third-party tracking. Specifically, we develop a “sock puppet” auditing system in which a pair of fresh browser profiles, first, visits web pages that reflect divergent political discourses and, second, executes identical politically oriented Google News searches. Comparing the search results returned by Google News for distinctly trained browser profiles, we observe statistically significant personalization that tends to reinforce the presumed partisanship.