Gender microaggressions are subtle yet persistent forms of discrimination in workplace interactions. While LLMs can detect them in written texts, it remains poorly understood how their interpretations align or diverge from human perspectives and experiences. We present a mixed-method study comparing how LLMs and humans differing in gender identity and lived experience, interpret gender microaggressions in the workplace. Using short dialogues adapted from real-world accounts, we asked 141 participants to rate the likelihood that a scenario contains a microaggression and provide a rationale for their answers. The same tasks were completed by 7 different LLM models. Our analysis reveals significant differences in how humans and LLMs interpret microaggressions, captured in both ratings and rationales, and more interestingly, the effect of gender and lived experience on human interpretations. These findings highlight the need for systems detecting microaggressions to embrace interpretive plurality, and support reflection and awareness while accounting for ambiguity.

As values shape the design and governance of technology, it becomes critical to move beyond universal framings to explore the nuanced, subjective understandings individuals hold about values. Traditional value elicitation methods often identify values at play but overlook how they are interpreted through individuals’ social identities and lived experiences. This paper introduces an AI-augmented value exploration method inspired by photo elicitation, which involves interviews supported by participant-taken photographs. Instead, we use AI-generated imagery to uncover hidden associations and insights around personal understandings of values. In an exploratory study with six participants, we focused on the value of well-being, examining how AI-generated visuals prompted diverse personal interpretations and facilitated deeper value reflections. Our findings show that this method uncovers implicit meanings and deepens discussions by translating abstract ideas into tangible interpretations to yield richer data on situated values.