Media representations of people with disabilities profoundly influence societal perceptions, yet have historically been absent, stereotyped, or inaccurate. As AI-generated visual media becomes increasingly prevalent, there is a critical opportunity to address these misrepresentations. Responding to the lack of collectively negotiated representation standards, this paper presents our human-centric approach to engaging disability communities meaningfully in AI data practices. Over three months, we worked closely with three disability organizations across the Global North and South to develop the Community Library Creator that introduces design scaffolds to support communities in defining ‘good’ representation and curating community-centric AI datasets; laying the foundations for community-specific evaluation metrics and future model adaptations. We contribute qualitative insights into the complexities of community-led data curation; discuss the value and practical challenges of intersecting human insights with AI requirements; and reflect on human-centered AI approaches that empower communities to share their perspectives and actively shape AI data practices.

Variance in predictions across different trained models is a significant, under-explored source of error in fair binary classification. In practice, the variance on some data examples is so large that decisions can be effectively arbitrary. To investigate this problem, we take an experimental approach and make four overarching contributions. We: 1) Define a metric called self-consistency, derived from variance, which we use as a proxy for measuring and reducing arbitrariness; 2) Develop an ensembling algorithm that abstains from classification when a prediction would be arbitrary; 3) Conduct the largest to-date empirical study of the role of variance (vis-a-vis self-consistency and arbitrariness) in fair binary classification; and, 4) Release a toolkit that makes the US Home Mortgage Disclosure Act (HMDA) datasets easily usable for future research. Altogether, our experiments reveal shocking insights about the reliability of conclusions on benchmark datasets. Most fair binary classification benchmarks are close-to-fair when taking into account the amount of arbitrariness present in predictions -- before we even try to apply any fairness interventions. This finding calls into question the practical utility of common algorithmic fairness methods, and in turn suggests that we should reconsider how we choose to measure fairness in binary classification.

In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through community collaboration, we explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions and codes by systematically reviewing 115 papers, while leveraging the expertise of researchers in various disciplines. Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the design of new writing assistants.

This paper examines the tensions between neighborhood gentrification and community surveillance posts on Nextdoor, a hyperlocal social media platform for neighborhoods. We created a privacy-preserving pipeline to gather research data from public Nextdoor posts in Atlanta, Georgia and filtered these to a dataset of 1,537 community surveillance posts. We developed a qualitative codebook to label observed patterns of community surveillance, and deploy a large language model to tag these posts at scale. Ultimately, we present an extensible and empirically-tested typology of the modes of community surveillance that occur on hyperlocal platforms. We find a complex relationship between community surveillance posts and neighborhood gentrification, which indicates that publicly disclosing information about perceived outsiders, especially for petty crimes, is most prevalent in gentrifying neighborhoods. Our empirical evidence inform critical perspectives which posit that community surveillance on platforms like Nextdoor can exclude and marginalize minoritized populations, particularly in gentrifying neighborhoods. Our findings carry broader implications for hyperlocal social platforms and their potential to amplify and exacerbate social tensions and exclusion.

The pervasive influence of online activities in our lives, encompassing personal connections, professional engagements, and e-commerce, has amplified concerns about privacy. However, existing privacy research has predominantly concentrated on the individual level, paying less attention to the privacy practices and strategies adopted by online social groups. This research gap calls for a renewed focus on understanding and addressing privacy challenges specific to online group settings. In this paper we explore the privacy needs of online groups through the lens of Contextual Integrity. We perform two complementary studies: semi-structured qualitative interviews of Facebook Groups users (n=17), and a large-scale survey of individuals organizing in groups on Facebook, Discord, and Reddit (n=4486). We investigate the privacy needs of different contextual groups, and locate the presence of contextual norms, contextual member roles, explicit and implicit rules, and privacy concerns. We trace how this complex interplay informs privacy expectations, needs, and negotiations across groups. We find that technical systems provide limited tools to effectively enforce group privacy, allowing individuals to compromise privacy norms. Based on these findings, we offer recommendations to support the design of privacy controls for online groups.