相关论文: Data Collectives as a means to Improve Accountabil…

Rising concern for the societal implications of artificial intelligence systems has inspired demands for greater transparency and accountability. However the datasets which empower machine learning are often used, shared and re-used with…

Lightweight fine-tuning techniques and the rise of 'open' AI model marketplaces have enabled individuals to easily build and release generative models. Yet, this accessibility also raises risks, including the production of harmful and…

When governments mandate collaboration, shared data systems can serve both as tools for coordination and instruments of control. This study examines U.S. homelessness service networks, where Continuums of Care (CoCs) coordinate service…

Digital collaborative platforms have become crucial venues of career advancement and individual success in many creative fields, from engineering to the arts. Indirect gender discrimination is a key component to gendered disadvantage on…

Recent research has helped to cultivate growing awareness that machine learning systems fueled by big data can create or exacerbate troubling disparities in society. Much of this research comes from outside of the practicing data science…

Qualitative inductive methods are widely used in CSCW and HCI research for their ability to generatively discover deep and contextualized insights, but these inherently manual and human-resource-intensive processes are often infeasible for…

Software development, often perceived as a technical endeavor, is fundamentally a social activity requiring collaboration among team members. Acknowledging this, the software development community has devised strategies to address possible…

Crowdworking platforms provide the opportunity for diverse workers to execute tasks for different requesters. The popularity of the "gig" economy has given rise to independent platforms that provide competing and complementary services.…

We investigate the privacy practices of labor organizers in the computing technology industry and explore the changes in these practices as a response to remote work. Our study is situated at the intersection of two pivotal shifts in…

Over the past decades, improvements in data collection hardware coupled with novel artificial intelligence algorithms have made it possible for researchers to understand urban environments at an unprecedented scale. From local interactions…

Digital labor platforms are increasingly used to procure human input, ranging from annotating data and red-teaming AI models, to ride-sharing and food delivery. A central concern in such markets is the ability of platforms to suppress wages…

Data-driven algorithmic and AI systems are increasingly being deployed to automate or augment decision processes across a wide range of public service settings. Yet community members are often unaware of the presence, operation, and impacts…

As learning systems increasingly shape everyday decisions, Algorithmic Collective Action (ACA), i.e., users coordinating changes to shared data to steer model behavior, offers a complement to regulator-side policy and corporate model…

Cloud Computing is rising fast, with its data centres growing at an unprecedented rate. However, this has come with concerns of privacy, efficiency at the expense of resilience, and environmental sustainability, because of the dependence on…

General-purpose crowdsourcing platforms are increasingly being harnessed for creative work. The platforms' potential for creative work is clearly identified, but the workers' perspectives on such work have not been extensively documented.…

Collaborative perception is essential to address occlusion and sensor failure issues in autonomous driving. In recent years, theoretical and experimental investigations of novel works for collaborative perception have increased…

The phrase "online harms" has emerged in recent years out of a growing political willingness to address the ethical and social issues associated with the use of the Internet and digital technology at large. The broad landscape that…

As platform ecosystems are growing by platform users' data, the importance of data governance has been highlighted. In particular, how to share control and decision rights with platform users are regarded as significant design issues since…

With the rapid demand of data and computational resources in deep learning systems, a growing number of algorithms to utilize collaborative machine learning techniques, for example, federated learning, to train a shared deep model across…

This article-based doctoral thesis explores the stakeholder perspectives and experiences of crowdsourced creative work on two of the leading crowdsourcing platforms. The thesis has two parts. In the first part, we explore creative work from…