Amy X. Zhang’s research while affiliated with University of Washington Tacoma and other places

Responding to misinformation online can be an exhausting and thankless task. It takes time and energy to write effective content, puts users at risk of online harassment, and strains personal relationships. Despite these challenges, there are people who voluntarily respond to misinformation online, and some have established communities on platforms such as Reddit, Discord, and X (formerly Twitter) dedicated to these efforts. In this work, we interviewed 8 people who participate in such communities to understand the type of support they receive from each other in these discussion spaces. Interviewees described that their communities helped them sustain motivation, save time, and improve their communication skills. Common practices included sharing sources and citations, providing emotional support, giving others advice, and signaling positive feedback. We present our findings as three case studies and discuss opportunities for future work to support collaborative practices in online communities dedicated to misinformation response. Our work surfaces how resource sharing, social motivation, and decentralization can make misinformation correction more sustainable, rewarding, and effective for online citizens.

Alignment of large language models (LLMs) to societal values should account for pluralistic values from diverse groups. One technique uses in-context learning for inference-time alignment, but only considers similarity when drawing few-shot examples, not accounting for cross-group differences in value prioritization. We propose SPICA, a framework for pluralistic alignment that accounts for group-level differences during in-context example retrieval. SPICA introduces three designs to facilitate pluralistic alignment: scenario banks, group-informed metrics, and in-context alignment prompts. From an evaluation of SPICA on an alignment task collecting inputs from four demographic groups ($n = 544$), our metrics retrieve in-context examples that more closely match observed preferences, with the best prompt configuration using multiple contrastive responses to demonstrate examples. In an end-to-end evaluation ($n = 80$), we observe that SPICA-aligned models are higher rated than a baseline similarity-only retrieval approach, with groups seeing up to a +0.16 point improvement on a 5 point scale. Additionally, gains from SPICA were more uniform, with all groups benefiting from alignment rather than only some. Finally, we find that while a group-agnostic approach can effectively align to aggregated values, it is not most suited for aligning to divergent groups.

We introduce a method to measure the alignment between public will and language model (LM) behavior that can be applied to fine-tuning, online oversight, and pre-release safety checks. Our `chain of alignment' (CoA) approach produces a rule based reward (RBR) by creating model behavior $\textit{rules}$ aligned to normative $\textit{objectives}$ aligned to $\textit{public will}$. This factoring enables a nonexpert public to directly specify their will through the normative objectives, while expert intelligence is used to figure out rules entailing model behavior that best achieves those objectives. We validate our approach by applying it across three different domains of LM prompts related to mental health. We demonstrate a public input process built on collective dialogues and bridging-based ranking that reliably produces normative objectives supported by at least $96\% \pm 2\%$ of the US public. We then show that rules developed by mental health experts to achieve those objectives enable a RBR that evaluates an LM response's alignment with the objectives similarly to human experts (Pearson's r=0.841, AUC=0.964). By measuring alignment with objectives that have near unanimous public support, these CoA RBRs provide an approximate measure of alignment between LM behavior and public will.

Creative writers have a love for their craft, yet AI systems using large language models (LLMs) offer the automation of significant parts of the writing process. So why do some creative writers choose to integrate AI into their workflows? To explore this, we interview and observe a writing session with 18 creative writers who already use AI regularly in their writing practice. Our findings reveal that creative writers are intentional about how they incorporate AI, making many deliberate decisions about when and how to engage AI based on the core values they hold about writing. These values, such as authenticity and craftsmanship, alongside writers' relationships with and use of AI influence the parts of writing over which they wish to maintain control. Through our analysis, we contribute a taxonomy of writer values, writer relationships with AI, and integration strategies, and discuss how these three elements interrelate.

AI agents are increasingly tasked with making proactive suggestions in online spaces where groups collaborate, but can be unhelpful or even annoying, due to not fitting the group's preferences or behaving in socially inappropriate ways. Fortunately, group spaces have a rich history of prior social interactions and affordances for social feedback to support creating agents that align to a group's interests and norms. We present Social-RAG, a workflow for grounding agents to social information about a group, which retrieves from prior group interactions, selects relevant social signals, and then feeds the context into a large language model to generate messages to the group. We implement this into PaperPing, our system that posts academic paper recommendations in group chat, leveraging social signals determined from formative studies with 39 researchers. From a three-month deployment in 18 channels, we observed PaperPing posted relevant messages in groups without disrupting their existing social practices, fostering group common ground.

The rise of large language models (LLMs) has led many researchers to consider their usage for scientific work. Some have found benefits using LLMs to augment or automate aspects of their research pipeline, while others have urged caution due to risks and ethical concerns. Yet little work has sought to quantify and characterize how researchers use LLMs and why. We present the first large-scale survey of 816 verified research article authors to understand how the research community leverages and perceives LLMs as research tools. We examine participants' self-reported LLM usage, finding that 81% of researchers have already incorporated LLMs into different aspects of their research workflow. We also find that traditionally disadvantaged groups in academia (non-White, junior, and non-native English speaking researchers) report higher LLM usage and perceived benefits, suggesting potential for improved research equity. However, women, non-binary, and senior researchers have greater ethical concerns, potentially hindering adoption.

Human-Computer Interaction (HCI) and Computer Supported Collaborative Work (CSCW) have a longstanding tradition of interrogating the values that underlie systems in order to create novel and accessible experiences. In this work, we use a neurodiversity framing to examine how people with ways of thinking, speaking, and being that differ from normative assumptions are perceived by researchers seeking to study and design social computing systems for neurodivergent people. From a critical analysis of 84 publications systematically gathered across a decade of social computing research, we determine that research into social computing with neurodiverse participants is largely medicalized, adheres to historical stereotypes of neurodivergent children and their families, and is insensitive to the wide spectrum of neurodivergent people that are potential users of social technologies. When social computing systems designed for neurodivergent people rely upon a conception of disability that restricts expression for the sake of preserving existing norms surrounding social experience, the result is often simplistic and restrictive systems that prevent users from "being social" in a way that feels natural and enjoyable. We argue that a neurodiversity perspective informed by critical disability theory allows us to engage with alternative forms of sociality as meaningful and desirable rather than a deficit to be compensated for. We conclude by identifying opportunities for researchers to collaborate with neurodivergent users and their communities, including the creation of spectrum-conscious social systems and the embedding of double empathy into systems for more equitable design.