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Crowd-work has increased significantly in recent years, particularly among women from Latin America. However, the specific needs and characteristics of this workforce have not been studied nearly enough. For this reason, we have conducted a series of surveys, questionnaires, and design sessions directly with Latin-American users of crowd-working platforms. Our aim was to create a system to empower crowd-workers with AI enhanced tools for their day-to-day tasks. As a result, we created a customized platform, La Independiente, and two web plugins. This project is unique in that it leverages gender perspective methodologies, AI powered-systems, and public policy analysis to design smart tools that are both professionally useful and culturally relevant. 

AI has revolutionized the processing of various services, including the automatic facial verification of people. Automated approaches have demonstrated their speed and efficiency in verifying a large volume of faces, but they can face challenges when processing content from certain communities, including communities of people of color. This challenge has prompted the adoption of "human-inthe-loop" (HITL) approaches, where human workers collaborate with the AI to minimize errors. However, most HITL approaches do not consider workers’ individual characteristics and backgrounds. This paper proposes a new approach, called Inclusive Portraits (IP), that connects with social theories around race to design a racially-aware human-in-the-loop system. Our experiments have provided evidence that incorporating race into human-in-the-loop (HITL) systems for facial verification can significantly enhance performance, especially for services delivered to people of color. Our findings also highlight the importance of considering individual worker characteristics in the design of HITL systems, rather than treating workers as a homogenous group. Our research has significant design implications for developing AI-enhanced services that are more inclusive and equitable. 

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. 

How do you decide which papers to cite, how many, and from which particular sources? We reflect and discuss the implications of these critical questions based on our experiences in the panel and workshops on the topic of citational justice that took place at CSCW, CLIHC, and India HCI in 2021. 

rowdsourcing has been used to produce impactful and large-scale datasets for Machine Learning and Artificial Intelligence (AI), such as ImageNET, SuperGLUE, etc. Since the rise of crowdsourcing in early 2000s, the AI community has been studying its computational, system design, and data-centric aspects at various angles. We welcome the studies on developing and enhancing of crowdworker-centric tools, that offer task matching, requester assessment, instruction validation, among other topics. We are also interested in exploring methods that leverage the integration of crowdworkers to improve the recognition and performance of the machine learning models. Thus, we invite studies that focus on shipping active learning techniques, methods for joint learning from noisy data and from crowds, novel approaches for crowd-computer interaction, repetitive task automation, and role separation between humans and machines. Moreover, we invite works on designing and applying such techniques in various domains, including e-commerce and medicine. 

The popularity of 3D printed assistive technology has led to the emergence of new ecosystems of care, where multiple stakeholders (makers, clinicians, and recipients with disabilities) work toward creating new upper limb prosthetic devices. However, despite the increasing growth, we currently know little about the differences between these care ecosystems. Medical regulations and the prevailing culture have greatly impacted how ecosystems are structured and stakeholders work together, including whether clinicians and makers collaborate. To better understand these care ecosystems, we interviewed a range of stakeholders from multiple countries, including Brazil, Chile, Costa Rica, France, India, Mexico, and the U.S. Our broad analysis allowed us to uncover different working examples of how multiple stakeholders collaborate within these care ecosystems and the main challenges they face. Through our study, we were able to uncover that the ecosystems with multi-stakeholder collaborations exist (something prior work had not seen), and these ecosystems showed increased success and impact. We also identified some of the key follow-up practices to reduce device abandonment. Of particular importance are to have ecosystems put in place follow up practices that integrate formal agreements and compensations for participation (which do not need to be just monetary). We identified that these features helped to ensure multi-stakeholder involvement and ecosystem sustainability. We finished the paper with socio-technical recommendations to create vibrant care ecosystems that include multiple stakeholders in the production of 3D printed assistive devices. 

The limited information (data voids) on political topics relevant to underrepresented communities has facilitated the spread of disinformation. Independent journalists who combat disinformation in underrepresented communities have reported feeling overwhelmed because they lack the tools necessary to make sense of the information they monitor to address the data voids. In this paper, we present a system to identify and address political data voids within underrepresented communities. Armed with an interview study indicating that independent news media has the potential of addressing these data voids, we designed the intelligent system: Datavoidant. Datavoidant introduces a novel design space that focuses on providing independent journalists with a collective understanding of data voids to then facilitate generating content to cover the voids. We performed a user interface evaluation with independent news media journalists (N=22). Journalists reported that Datavoidant's features allowed them to more rapidly and easily have a sense of what was taking place in the information ecosystem to address the data voids; they also reported feeling more confident about the content they created and the unique perspectives they proposed to cover the voids. We finish by discussing how Datavoidant enables a new design space where individuals can collaboratively make sense of their information ecosystem, and can proactively devise strategies for uniquely contributing information to their ecosystem, and together prevent disinformation.