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1. The Challenges of Crowd Workers in Rural and Urban America

   Saviaga, C ; Li, Y ; Hanrahan, B ; Bigham, J ; Savage, S. ( January 2020 , Human computation)

   null (Ed.)

   Crowd work has the potential of helping the financial recovery of regions traditionally plagued by a lack of economic opportunities, e.g., rural areas. However, we currently have limited information about the challenges facing crowd workers from rural and super rural areas as they struggle to make a living through crowd work sites. This paper examines the challenges and advantages of rural and super rural Amazon Mechanical Turk (MTurk) crowd workers and contrasts them with those of workers from urban areas. Based on a survey of 421 crowd workers from differing geographic regions in the U.S., we identified how across regions, people struggled with being onboarded into crowd work. We uncovered that despite the inequalities and barriers, rural workers tended to be striving more in micro-tasking than their urban counterparts. We also identified cultural traits, relating to time dimension and individualism, that offer us an insight into crowd workers and the necessary qualities for them to succeed on gig platforms. We finish by providing design implications based on our findings to create more inclusive crowd work platforms and tools. 

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2. A Tale of Two Communities: Privacy of Third Party App Users in Crowdsourcing - The Case of Receipt Transcription

   https://doi.org/10.1145/3610044  

   Pei, Weiping ; Likhtenshteyn, Yanina ; Yue, Chuan ( September 2023 , Proceedings of the ACM on Human-Computer Interaction)

   Mobile and web apps are increasingly relying on the data generated or provided by users such as from their uploaded documents and images. Unfortunately, those apps may raise significant user privacy concerns. Specifically, to train or adapt their models for accurately processing huge amounts of data continuously collected from millions of app users, app or service providers have widely adopted the approach of crowdsourcing for recruiting crowd workers to manually annotate or transcribe the sampled ever-changing user data. However, when users' data are uploaded through apps and then become widely accessible to hundreds of thousands of anonymous crowd workers, many human-in-the-loop related privacy questions arise concerning both the app user community and the crowd worker community. In this paper, we propose to investigate the privacy risks brought by this significant trend of large-scale crowd-powered processing of app users' data generated in their daily activities. We consider the representative case of receipt scanning apps that have millions of users, and focus on the corresponding receipt transcription tasks that appear popularly on crowdsourcing platforms. We design and conduct an app user survey study (n=108) to explore how app users perceive privacy in the context of using receipt scanning apps. We also design and conduct a crowd worker survey study (n=102) to explore crowd workers' experiences on receipt and other types of transcription tasks as well as their attitudes towards such tasks. Overall, we found that most app users and crowd workers expressed strong concerns about the potential privacy risks to receipt owners, and they also had a very high level of agreement with the need for protecting receipt owners' privacy. Our work provides insights on app users' potential privacy risks in crowdsourcing, and highlights the need and challenges for protecting third party users' privacy on crowdsourcing platforms. We have responsibly disclosed our findings to the related crowdsourcing platform and app providers.

    

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3. High royal jelly production does not impact the gut microbiome of honey bees

   https://doi.org/10.1186/s42523-021-00124-1  

   Damico, Megan E. ; Rueppell, Olav ; Shaffer, Zack ; Han, Bin ; Raymann, Kasie ( September 2021 , Animal Microbiome)

   Honey bees are not only essential for pollination services, but are also economically important as a source of hive products (e.g., honey, royal jelly, pollen, wax, and propolis) that are used as foods, cosmetics, and alternative medicines. Royal jelly is a popular honey bee product with multiple potential medicinal properties. To boost royal jelly production, a long-term genetic selection program of Italian honey bees (ITBs) in China has been performed, resulting in honey bee stocks (here referred to as RJBs) that produce an order of magnitude more royal jelly than ITBs. Although multiple studies have investigated the molecular basis of increased royal jelly yields, one factor that has not been considered is the role of honey bee-associated gut microbes.

   Based on the behavioral, morphological, physiological, and neurological differences between RJBs and ITBs, we predicted that the gut microbiome composition of RJBs bees would differ from ITBs. To test this hypothesis, we investigated the bacterial composition of RJB and ITB workers from an urban location and RJBs from a rural location in China. Based on 16S rRNA gene profiling, we did not find any evidence that RJBs possess a unique bacterial gut community when compared to ITBs. However, we observed differences between honey bees from the urban versus rural sites.

   Our results suggest that the environmental factors rather than stock differences are more important in shaping the bacterial composition in honey bee guts. Further studies are needed to investigate if the observed differences in relative abundance of taxa between the urban and rural bees correspond to distinct functional capabilities that impact honey bee health. Because the lifestyle, diet, and other environmental variables are different in rural and urban areas, controlled studies are needed to determine which of these factors are responsible for the observed differences in gut bacterial composition between urban and rural honeybees.

    

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4. La Independiente: Designing Ubiquitous Systems for Latin American and Caribbean Women Crowdworkers

   https://doi.org/10.1145/3594739.3610728  

   De Los Santos, Maya ; Chávez, Norma Elva ; Navarrete, Alberto ; Martínez Pinto, Cristina ; González, Luz Elena ; Telles-Calderon, Tatiana ; Savage, Saiph ( October 2023 , ACM Ubicomp for All Symposium)

   Since 2018, Venezuelans have contributed to 75% of leading AI crowd work platforms’ total workforce, and it is very likely other Latin American and Caribbean (LAC) countries will follow in the context of the post covid-19 economic recovery. While crowd work presents new opportunities for employment in regions of the world where local economies have stagnated, few initiatives have investigated the impact of such work in the Global South through the lens of feminist theory. To address this knowledge gap, we surveyed 55 LAC women on the crowd work platform Toloka to understand their personal goals, professional values, and hardships faced in their work. Our results revealed that most participants shared a desire to hear the experiences of other women crowdworkers, mainly to help them navigate tasks, develop technical and soft skills, and manage their finances more efficiently. Additionally, 75% of the women reported that they completed crowd work tasks on top of caring for their families, while over 50% confirmed they needed to negotiate their family responsibilities to pursue crowd work in the first place. These findings demonstrated a vital component lacking from the experiences of these women was a sense of connection with one another. Based on these observations, we propose a system designed to foster community between LAC women in crowd work to improve their personal and professional advancement. 

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5. Progression of COVID‐19 From Urban to Rural Areas in the United States: A Spatiotemporal Analysis of Prevalence Rates

   https://doi.org/10.1111/jrh.12486  

   Paul, Rajib ; Arif, Ahmed A. ; Adeyemi, Oluwaseun ; Ghosh, Subhanwita ; Han, Dan ( June 2020 , The Journal of Rural Health)

   There are growing signs that the COVID‐19 virus has started to spread to rural areas and can impact the rural health care system that is already stretched and lacks resources. To aid in the legislative decision process and proper channelizing of resources, we estimated and compared the county‐level change in prevalence rates of COVID‐19 by rural‐urban status over 3 weeks. Additionally, we identified hotspots based on estimated prevalence rates.

   We used crowdsourced data on COVID‐19 and linked them to county‐level demographics, smoking rates, and chronic diseases. We fitted a Bayesian hierarchical spatiotemporal model using the Markov Chain Monte Carlo algorithm in R‐studio. We mapped the estimated prevalence rates using ArcGIS 10.8, and identified hotspots using Gettis‐Ord local statistics.

   In the rural counties, the mean prevalence of COVID‐19 increased from 3.6 per 100,000 population to 43.6 per 100,000 within 3 weeks from April 3 to April 22, 2020. In the urban counties, the median prevalence of COVID‐19 increased from 10.1 per 100,000 population to 107.6 per 100,000 within the same period. The COVID‐19 adjusted prevalence rates in rural counties were substantially elevated in counties with higher black populations, smoking rates, and obesity rates. Counties with high rates of people aged 25‐49 years had increased COVID‐19 prevalence rates.

   Our findings show a rapid spread of COVID‐19 across urban and rural areas in 21 days. Studies based on quality data are needed to explain further the role of social determinants of health on COVID‐19 prevalence.

    

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