Online communities serve as essential support channels for People Who Use Drugs (PWUD), providing access to peer support and harm reduction information. The moderation of these communities involves consequential decisions affecting member safety, yet existing sociotechnical systems provide insufficient support for moderators. Through interviews with experienced moderators from PWUD forums on Reddit, we examine the unique nature of this work and its implications for HCI and content moderation research. We demonstrate that this work constitutes a distinct form of public health intervention characterised by three challenges: (1) high-stakes risk evaluation requiring pharmacological expertise, (2) time-critical crisis intervention spanning platform content and external drug market surveillance, and (3) navigation of structural conflicts where platform policies designed to minimise legal liability directly oppose community harm reduction goals. Our findings extend existing HCI moderation frameworks by revealing how legal liability structures can systematically undermine expert moderators' protective work, with implications for other marginalised communities facing similar regulatory tensions, including abortion care and sex work contexts. We identify two necessary shifts in sociotechnical design: moving from binary classification to multi-dimensional approaches that externalise competing factors moderators must balance, and shifting from low-level rule programming to high-level example-based instruction. However, we surface unresolved tensions around volunteer labour sustainability and risks of incorporating automated systems in high-stakes health contexts, identifying open questions requiring HCI research attention. These findings inform the design of platforms that better accommodate vulnerable populations whose health needs conflict with regulatory frameworks.

Augmented Reality (AR) can deliver engaging user experiences that seamlessly meld virtual content with the physical environment. However, building such experiences is challenging due to the developer’s inability to assess how uncontrolled deployment contexts may influence the user experience. To address this issue, we demonstrate a method for rapidly conducting AR experiments and real-world data collection in the user’s own physical environment using a privacy-conscious mobile web application. The approach leverages the large number of distinct user contexts accessible through crowdsourcing to efficiently source diverse context and perceptual preference data. The insights gathered through this method complement emerging design guidance and sample-limited lab-based studies. The utility of the method is illustrated by re-examining the design challenge of adapting AR text content to the user’s environment. Finally, we demonstrate how gathered design insight can be operationalized to provide adaptive text content functionality in an AR headset.

Crowdwork can enable invaluable opportunities for people with disabilities, not least the work flexibility and the ability to work from home, especially during the current Covid-19 pandemic. This paper investigates how engagement in crowdwork tasks is affected by individual disabilities and the resulting implications for HCI. We first surveyed 1,000 Amazon Mechanical Turk (AMT) workers to identify demographics of crowdworkers who identify as having various disabilities within the AMT ecosystem—including vision, hearing, cognition/mental, mobility, reading and motor impairments. Through a second focused survey and follow-up interviews, we provide insights into how respondents cope with crowdwork tasks. We found that standard task factors, such as task completion time and presentation, often do not account for the needs of users with disabilities, resulting in anxiety and a feeling of depression on occasion. We discuss how to alleviate barriers to enable effective interaction for crowdworkers with disabilities.

Designing novel interfaces is challenging. Designers typically rely on experience or subjective judgment in the absence of analytical or objective means for selecting interface parameters. We demonstrate Bayesian optimization as an efficient tool for objective interface feature refinement. Specifically, we show that crowdsourcing paired with Bayesian optimization can rapidly and effectively assist interface design across diverse deployment environments. Experiment 1 evaluates the approach on a familiar 2D interface design problem: a map search and review use case. Adding a degree of complexity, Experiment 2 extends Experiment 1 by switching the deployment environment to mobile-based virtual reality. The approach is then demonstrated as a case study for a fundamentally new and unfamiliar interaction design problem: web-based augmented reality. Finally, we show how the model generated as an outcome of the refinement process can be used for user simulation and queried to deliver various design insights.

The nature of work is changing. As labor increasingly trends to casual work in the emerging gig economy, understanding the broader economic context is crucial to effective engagement with a contingent workforce. Crowdsourcing represents an early manifestation of this fluid, laisser-faire, on-demand workforce. This work analyzes the results of four large-scale surveys of US-based Amazon Mechanical Turk workers recorded over a six-year period, providing comparable measures to national statistics. Our results show that despite unemployment far higher than national levels, crowdworkers are seeing positive shifts in employment status and household income. Our most recent surveys indicate a trend away from full-time-equivalent crowdwork, coupled with a reduction in estimated poverty levels to below national figures. These trends are indicative of an increasingly flexible workforce, able to maximize their opportunities in a rapidly changing national labor market, which may have material impacts on existing models of crowdworker behavior.

The ability to entice and engage crowd workers to participate in human intelligence tasks (HITs) is critical for many human computation systems and large-scale experiments. While various metrics have been devised to measure and improve the quality of worker output via task designs, effective recruitment of crowd workers is often overlooked. To help us gain a better understanding of crowd recruitment strategies we propose three new metrics for measuring crowd workers' willingness to participate in advertised HITs: conversion rate, conversion rate over time, and nominal conversion rate. We discuss how the conversion rate of workers—the number of potential workers aware of a task that choose to accept the task—can affect the quantity, quality, and validity of any data collected via crowdsourcing. We also contribute a tool — turkmill — that enables requesters on Amazon Mechanical Turk to easily measure the conversion rate of HITs. We then present the results of two experiments that demonstrate how conversion rate metrics can be used to evaluate the effect of different HIT designs. We investigate how four HIT design features (value proposition, branding, quality of presentation, and intrinsic motivation) affect conversion rates. Among other things, we find that including a clear value proposition has a strong significant, positive effect on the nominal conversion rate. We also find that crowd workers prefer commercial entities to non-profit or university requesters.