相关论文: Design Activism for Minimum Wage Crowd Work

While Amazon's Mechanical Turk (AMT) helped launch the paid crowd work industry eight years ago, many new vendors now offer a range of alternative models. Despite this, little crowd work research has explored other platforms. Such…

We study the causal effects of financial incentives on the quality of crowdwork. We focus on performance-based payments (PBPs), bonus payments awarded to workers for producing high quality work. We design and run randomized behavioral…

Despite a plethora of research dedicated to designing HITs for non-workstations, there is a lack of research looking specifically into workers' perceptions of the suitability of these devices for managing and completing work. In this work,…

How can we better understand the broad, diverse, shifting, and invisible crowd workforce, so that we can better support it? We present findings from online observations and analysis of publicly available postings from a community forum of…

In 2013, scholars laid out a framework for a sustainable, ethical future of crowd work, recommending career ladders so that crowd work can lead to career advancement and more economic mobility. Five years later, we consider this vision in…

Fairness in AI and ML systems is increasingly linked to the proper treatment and recognition of data workers involved in training dataset development. Yet, those who collect and annotate the data, and thus have the most intimate knowledge…

Over the past decade, Big Tech has faced increasing levels of worker activism. While worker actions have resulted in positive outcomes (e.g., cancellation of Google's Project Dragonfly), such successes have become increasingly infrequent.…

Current crowdsourcing platforms provide little support for worker feedback. Workers are sometimes invited to post free text describing their experience and preferences in completing tasks. They can also use forums such as Turker Nation1 to…

Crowdsourcing has become an important tool to collect data for various artificial intelligence applications and auction can be an effective way to allocate work and determine reward in a crowdsourcing platform. In this paper, we focus on…

While microtask crowdsourcing provides a new way to solve large volumes of small tasks at a much lower price compared with traditional in-house solutions, it suffers from quality problems due to the lack of incentives. On the other hand,…

How should we decide which fairness criteria or definitions to adopt in machine learning systems? To answer this question, we must study the fairness preferences of actual users of machine learning systems. Stringent parity constraints on…

The proliferating adoption of platform-based gig work increasingly raises concerns for worker conditions. Past studies documented how platforms leveraged design to exploit labor, withheld information to generate power asymmetries, and left…

Crowdsourcing is a common approach to rapidly annotate large volumes of data in machine learning applications. Typically, crowd workers are compensated with a flat rate based on an estimated completion time to meet a target hourly wage.…

Since its emergence roughly a decade ago, microtask crowdsourcing has been attracting a heterogeneous set of workers from all over the globe. This paper sets out to explore the characteristics of the international crowd workforce and offers…

The gig economy is characterized by short-term contract work completed by independent workers who are paid to perform "gigs", and who have control over when, whether and how they conduct work. Gig economy platforms (e.g., Uber, Lyft,…

Crowd sensing is a new paradigm which leverages the pervasive smartphones to efficiently collect and upload sensing data, enabling numerous novel applications. To achieve good service quality for a crowd sensing application, incentive…

Traditionally, the impact of minimum wages on employment has been studied, and it is generally believed to have a negative effect. Yet, some recent studies have shown that the impact of minimum wages on employment can sometimes be positive.…

Crowdsourcing provides a popular paradigm for data collection at scale. We study the problem of selecting subsets of workers from a given worker pool to maximize the accuracy under a budget constraint. One natural question is whether we…

Rideshare platforms exert significant control over workers through algorithmic systems that can result in financial, emotional, and physical harm. What steps can platforms, designers, and practitioners take to mitigate these negative…

The global AI surge demands crowdworkers from diverse languages and cultures. They are pivotal in labeling data for enabling global AI systems. Despite global significance, research has primarily focused on understanding the perspectives…