By identifying the socio-technical conditions required for teams to work effectively remotely, the Distance Matters framework has been influential in CSCW since its introduction in 2000. Advances in collaboration technology and practices have since brought teams increasingly closer to achieving these conditions. This paper presents a ten-month ethnography in a remote organization, where we observed that despite exhibiting excellent remote collaboration, teams paradoxically struggled to collaborate across team boundaries. We extend the Distance Matters framework to account for inter-team collaboration, arguing that challenges analogous to those in the original intra-team framework --- common ground, collaboration readiness, collaboration technology readiness, and coupling of work --- persist but are actualized differently at the inter-team scale. Finally, we identify a fundamental tension between the intra- and inter-team layers: the collaboration technology and practices that help individual teams thrive (e.g., adopting customized collaboration software) can also prompt collaboration challenges in the inter-team layer, and conversely the technology and practices that facilitate inter-team collaboration (e.g., strong centralized IT organizations) can harm practices at the intra-team layer. The addition of the inter-team layer to the Distance Matters framework opens new opportunities for CSCW, where balancing the tension between team and organizational collaboration needs will be a critical technological, operational, and organizational challenge for remote work in the coming decades.

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A jury of one’s peers is a prominent way to adjudicate disputes and is increasingly used in participatory governance online. The fairness of this approach rests on the assumption that juries are consistent: that the same jury would hand down similar judgments to similar cases. However, prior literature suggests that social influence would instead cause early interactions to cascade into different judgments for similar cases. In this paper, we report an online experiment that changes participants’ pseudonyms as they appear to collaborators, temporarily masking a jury’s awareness that they have deliberated together before. This technique allows us to measure consistency by reconvening the same jury on similar cases. Counter to expectation, juries are equally consistent as individuals, a result that is “good for democracy.” But this consistency arises in part due to group polarization, as consensus develops by hardening initial majority opinions. Furthermore, we find that aggregating groups’ perspectives without deliberation erodes consistency.

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Gender bias has been a pervasive issue in U.S. political coverage since women gained the franchise in 1920. Women are routinely targeted for their appearance, likeability, and familial qualities—characteristics for which men are rarely scrutinized. As a way to help journalists and editors identify and correct gender-biased language in political reporting, we introduce “Disarming Loaded Words” (DLW) as a new solution to this issue. DLW is a computational tool, backed by both machine learning and human expert curation, that tags potentially biased words in a document and provides feedback and context for why a word may be problematic. DLW provides a minimally disruptive way to nudge journalists toward understanding unconscious biases. It was successfully prototyped as a standalone Google Docs add-on, and the code for its model, interface, and API are now open-source and available for use on other platforms.

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Deception detection is a difficult task, both for humans and machine learning models. In this paper, we contribute the novel NLU task of detecting social influence-based disingenuity in group discussions. We present a novel dataset for this task, on which we test three state-of-the-art machine learning models with two optimization schemes. We additionally benchmark our performance against humans. The task we propose is challenging; although several of our models outperform statistical and human baselines, overall performance leaves room to be desired. This task and dataset, therefore, remain an open challenge for NLU research.

This was our final project for the course CS224U: Natural Language Understanding at Stanford University.

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As a platform, Slack has almost no built-in content moderation. On the vanilla Slack platform, it's possible to curse out your teammates, post hateful content, and disseminate misinformation with impunity.

Thus, enter ModBotHero6. We combine a little bit of Reddit moderation bot with a little bit of Baymax—the friendly robot from Big Hero 6 that always asks its users whether they're 'satisfied with their care.' Like Baymax, our goal here is to put humans first. Our philosophy was to use the AI-powered moderation bot to remove or reduce immediate potential threats (i.e., hate speech, sexually explicit material, etc.) while also clearly signalling our understanding that AI isn't perfect. Some uses of profanity are positive, or appropriate given context—nuances that AI cannot yet detect. As such, the content we moderate goes to a human moderator for final approval. Each moderation report contains the API scores, in addition to a link to the message for context and bot-generated suggested actions.

We also realize that AI isn't perfect at catching every instance of abuse. Some types of abuse are nuanced, and hate speech is almost always culturally-specific. As a result, this bot also includes a flow for users to report abusive content, which, in turn, also gets directed to the moderator channel.

This project was selected as one of the top two projects in the course CS 152 (Trust and Safety Engineering) at Stanford. We were featured on the Stanford Internet Observatory's Live Webinar in March 2020! Check it out here.

Online petitions are a powerful but at times deeply flawed digital space. Despite the increasing importance of online petitions in galvanizing support for key issues, over 99% of petitions fail to achieve their goal, and the top 5% of users on Change.org (a popular online petitioning website) write 50% of the signatures. As a result, engagement in online petitions is incredibly uneven, and there are far more passive observers than active petitioners.

We introduce Zeal, a mobile application that encourages the otherwise passive consumer of content to take social action in the online petition space. Zeal combines news, a commonly consumed type of mobile content, with petitions related to the news articles. Drawing from the Fogg Behavior Model, we hypothesized that users who were invested in the news (motivated), faced with a simple ask (signing a petition), and given a prompt (Zeal’s news/petitions interface) would increase their petition-signing behavior. We then conducted a 10-day field study, through which we validated Zeal as a proof-of-concept for encouraging social action, but found that news alone is insufficient to fully motivate petition-signing behavior.

This project won three prizes — Best Field Study, Best Design, and Best Overall — in CS 377U: Understanding Users, evaluated by industry experts from Google, Yahoo, and Facebook.

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Workers on Amazon Mechanical Turk often find it impossible to prove their qualifications and find decent-paying work. Vitae is a resume management system for online gig workers, enabling them to achieve higher wages and more meaningful work.

This project was conducted through the CURIS (Undergraduate CS Research) Program.