Katharina Reinecke’s research while affiliated with University of Washington and other places

As large language models (LLMs) increasingly adapt and personalize to diverse sets of users, there is an increased risk of systems appropriating sociolects, i.e., language styles or dialects that are associated with specific minoritized lived experiences (e.g., African American English, Queer slang). In this work, we examine whether sociolect usage by an LLM agent affects user reliance on its outputs and user perception (satisfaction, frustration, trust, and social presence). We designed and conducted user studies where 498 African American English (AAE) speakers and 487 Queer slang speakers performed a set of question-answering tasks with LLM-based suggestions in either standard American English (SAE) or their self-identified sociolect. Our findings showed that sociolect usage by LLMs influenced both reliance and perceptions, though in some surprising ways. Results suggest that both AAE and Queer slang speakers relied more on the SAE agent, and had more positive perceptions of the SAE agent. Yet, only Queer slang speakers felt more social presence from the Queer slang agent over the SAE one, whereas only AAE speakers preferred and trusted the SAE agent over the AAE one. These findings emphasize the need to test for behavioral outcomes rather than simply assume that personalization would lead to a better and safer reliance outcome. They also highlight the nuanced dynamics of minoritized language in machine interactions, underscoring the need for LLMs to be carefully designed to respect cultural and linguistic boundaries while fostering genuine user engagement and trust.

Psychological research has identified different patterns individuals have while making decisions, such as vigilance (making decisions after thorough information gathering), hypervigilance (rushed and anxious decision-making), and buckpassing (deferring decisions to others). We examine whether these decision-making patterns shape peoples' likelihood of seeking out or relying on AI. In an online experiment with 810 participants tasked with distinguishing food facts from myths, we found that a higher buckpassing tendency was positively correlated with both seeking out and relying on AI suggestions, while being negatively correlated with the time spent reading AI explanations. In contrast, the higher a participant tended towards vigilance, the more carefully they scrutinized the AI's information, as indicated by an increased time spent looking through the AI's explanations. These findings suggest that a person's decision-making pattern plays a significant role in their adoption and reliance on AI, which provides a new understanding of individual differences in AI-assisted decision-making.

AI-supported writing technologies (AISWT) that provide grammatical suggestions, autocomplete sentences, or generate and rewrite text are now a regular feature integrated into many people's workflows. However, little is known about how people perceive the suggestions these tools provide. In this paper, we investigate how Black American users perceive AISWT, motivated by prior findings in natural language processing that highlight how the underlying large language models can contain racial biases. Using interviews and observational user studies with 13 Black American users of AISWT, we found a strong tradeoff between the perceived benefits of using AISWT to enhance their writing style and feeling like "it wasn't built for us". Specifically, participants reported AISWT's failure to recognize commonly used names and expressions in African American Vernacular English, experiencing its corrections as hurtful and alienating and fearing it might further minoritize their culture. We end with a reflection on the tension between AISWT that fail to include Black American culture and language, and AISWT that attempt to mimic it, with attention to accuracy, authenticity, and the production of social difference.

As modern AI models become integral to everyday tasks, concerns about their inherent biases and their potential impact on human decision-making have emerged. While bias in models are well-documented, less is known about how these biases influence human decisions. This paper presents two interactive experiments investigating the effects of partisan bias in AI language models on political decision-making. Participants interacted freely with either a biased liberal, conservative, or unbiased control model while completing political decision-making tasks. We found that participants exposed to politically biased models were significantly more likely to adopt opinions and make decisions aligning with the AI's bias, regardless of their personal political partisanship. However, we also discovered that prior knowledge about AI could lessen the impact of the bias, highlighting the possible importance of AI education for robust bias mitigation. Our findings not only highlight the critical effects of interacting with biased AI and its ability to impact public discourse and political conduct, but also highlights potential techniques for mitigating these risks in the future.

Postcolonial and decolonial computing examines how technology design and adoption can perpetuate subtle dimensions of coloniality, under-represent certain regions (e.g., the Global South, non-Western regions, Indigenous societies), and marginalize them. There has been a growing interest in interdisciplinary research focusing on marginalized communities, including accessibility and participatory research. Despite the rapid expansion of accessibility research in the last decades, little focus is placed on accessibility issues within marginalized societies, hindering them from effectively benefiting from accessibility research discussions and outcomes. The accessibility and HCI communities still lack comprehensive knowledge on conducting interdisciplinary research that includes diverse cultures and experiences from some of the systematically marginalized regions. This workshop will explore the intersection of accessibility, HCI, and cross-regional studies, bringing together researchers and practitioners to foster collaborations, identify under-explored research areas, and develop guidelines to support inclusive research practices.