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Crowdsourcing markets are expanding worldwide, but often feature standardized interfaces that ignore the cultural diversity of their workers, negatively impacting their well-being and productivity. To transform these workplace dynamics, this paper proposes creating culturally-aware workplace tools, specifically designed to adapt to the cultural dimensions of monochronic and polychronic work styles. We illustrate this approach with CultureFit, a tool that we engineered based on extensive research in Chronemics and culture theories. To study and evaluate our tool in the real world, we conducted a field experiment with 55 workers from 24 different countries. Our field experiment revealed that CultureFit significantly improved the earnings of workers from cultural backgrounds often overlooked in design. Our study is among the pioneering efforts to examine culturally aware digital labor interventions. It also provides access to a dataset with over two million data points on culture and digital work, which can be leveraged for future research in this emerging field. The paper concludes by discussing the importance and future possibilities of incorporating cultural insights into the design of tools for digital labor. 

This paper studies the design of an AI tool that supports gig knowledge workers, rather than displacing them, focusing on text-based generative AI technologies. Through a formative study involving interviews and design activities, gig workers shared their views on text-based generative AI and envisioned applications where AI acts as managers, secretaries, and communication aids. Leveraging these insights, we created a generative-AI enhanced tool, Office-Mind AI, to aid gig workers. Our research advances the conversation around algorithmic labor by designing a worker-focused intelligent tool. This tool harness collective intelligence among workers and AI, fostering productive human-AI partnerships. We conclude by discussing the future prospects of collective intelligence tools designed for worker-AI collaborations. 

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. 

Programmable microfluidic laboratories-on-a-chip (LoCs) offer the benefits of automation and miniaturization to the life sciences. This paper presents an updated version of the BioCoder language and a fully static (offline) compiler that can target an emerging class of LoCs called Digital Microfluidic Biochips (DMFBs), which manipulate discrete droplets of liquid on a 2D electrode grid. The BioCoder language and runtime execution engine leverage advances in sensor integration to enable specification, compilation, and execution of assays (bio-chemical procedures) that feature online decision-making based on sensory data acquired during assay execution. The compiler features a novel hybrid intermediate representation (IR) that interleaves fluidic operations with computations performed on sensor data. The IR extends the traditional notions of liveness and interference to fluidic variables and operations, as needed to target the DMFB, which itself can be viewed as a spatially reconfigurable array. The code generator converts the IR into the following: (1) a set of electrode activation sequences for each basic block in the control flow graph (CFG); (2) a set of computations performed on sensor data, which dynamically determine the result of each control flow operation; and (3) a set of electrode activation sequences for each control flow transfer operation (CFG edge). The compiler is validated using a software simulator which produces animated videos of realistic bioassay execution on a DMFB.