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Rhema Linder

Publications and source records attributed to Rhema Linder.

7 recordsLinked to original sources

Exploring Crowdworkers' Perceptions, Current Practices, and Desired Practices Regarding Using Non-Workstation Devices for Crowdwork

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, we fill this research gap by conducting an online survey of 148 workers on Amazon Mechanical Turk to explore 1. how crowdworkers currently use their non-workstation devices to complete and manage crowdwork, 2. what challenges they face using those devices, and 3. to what extent they wish they could use those devices if their concerns were addressed. Our results show that workers unanimously favor using a desktop to complete and manage crowdwork. While workers occasionally use smartphones or tablets, they find their suitability marginal at best and have little interest in smart speakers and smartwatches, viewing them as unsuitable for crowdwork. When investigating the reason for these views, we find that the key issue is that non workstation devices lack the tooling necessary to automatically find and accept HITs, tooling that workers view as essential in their efforts to compete with bots in accepting high paying work. To address this problem, we propose a new paradigm for finding, accepting, and completing crowdwork that puts crowdworkers on equal footing with bots in these tasks. We also describe future research directions for tailoring HITs to non workstation devices and definitely answering whether smart speakers and smartwatches have a place in crowdwork.

cs.HC

Unveiling the Inter-Related Preferences of Crowdworkers: Implications for Personalized and Flexible Platform Design

Crowdsourcing platforms have traditionally been designed with a focus on workstation interfaces, restricting the flexibility that crowdworkers need. Recognizing this limitation and the need for more adaptable platforms, prior research has highlighted the diverse work processes of crowdworkers, influenced by factors such as device type and work stage. However, these variables have largely been studied in isolation. Our study is the first to explore the interconnected variabilities among these factors within the crowdwork community. Through a survey involving 150 Amazon Mechanical Turk crowdworkers, we uncovered three distinct groups characterized by their interrelated variabilities in key work aspects. The largest group exhibits a reliance on traditional devices, showing limited interest in integrating smartphones and tablets into their work routines. The second-largest group also primarily uses traditional devices but expresses a desire for supportive tools and scripts that enhance productivity across all devices, particularly smartphones and tablets. The smallest group actively uses and strongly prefers non-workstation devices, especially smartphones and tablets, for their crowdworking activities. We translate our findings into design insights for platform developers, discussing the implications for creating more personalized, flexible, and efficient crowdsourcing environments. Additionally, we highlight the unique work practices of these crowdworker clusters, offering a contrast to those of more traditional and established worker groups.

cs.HC

Text-to-Image Generation: Perceptions and Realities

Generative AI is an emerging technology that will have a profound impact on society and individuals. Only a decade ago, it was thought that creative work would be among the last to be automated - yet today, we see AI encroaching on creative domains. In this paper, we present the key findings of a survey study on people's perceptions of text-to-image generation. We touch on participants' technical understanding of the emerging technology, their ideas for potential application areas, as well as concerns, risks, and dangers of text-to-image generation to society and the individual. The study found that participants were aware of the risks and dangers associated with the technology, but only few participants considered the technology to be a risk to themselves. Additionally, those who had tried the technology rated its future importance lower than those who had not.

cs.HC

Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering

We are witnessing a novel era of creativity where anyone can create digital content via prompt-based learning (known as prompt engineering). This paper investigates prompt engineering as a novel creative skill for creating AI art with text-to-image generation. In three consecutive studies, we explore whether crowdsourced participants can 1) discern prompt quality, 2) write prompts, and 3) refine prompts. We find that participants could evaluate prompt quality and crafted descriptive prompts, but they lacked style-specific vocabulary necessary for effective prompting. This is in line with our hypothesis that prompt engineering is a new type of skill that is non-intuitive and must first be acquired (e.g., through means of practice and learning) before it can be used. Our studies deepen our understanding of prompt engineering and chart future research directions. We conclude by envisioning four potential futures for prompt engineering.

cs.HC

A Neophyte With AutoML: Evaluating the Promises of Automatic Machine Learning Tools

This paper discusses modern Auto Machine Learning (AutoML) tools from the perspective of a person with little prior experience in Machine Learning (ML). There are many AutoML tools both ready-to-use and under development, which are created to simplify and democratize usage of ML technologies in everyday life. Our position is that ML should be easy to use and available to a greater number of people. Prior research has identified the need for intuitive AutoML tools. This work seeks to understand how well AutoML tools have achieved that goal in practice. We evaluate three AutoML Tools to evaluate the end-user experience and system performance. We evaluate the tools by having them create models from a competition dataset on banking data. We report on their performance and the details of our experience. This process provides a unique understanding of the state of the art of AutoML tools. Finally, we use these experiences to inform a discussion on how future AutoML tools can improve the user experience for neophytes of Machine Learning.

cs.LG

XFake: Explainable Fake News Detector with Visualizations

In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility. To effectively detect and interpret the fakeness of news items, we jointly consider both attributes (e.g., speaker) and statements. Specifically, MIMIC, ATTN and PERT frameworks are designed, where MIMIC is built for attribute analysis, ATTN is for statement semantic analysis and PERT is for statement linguistic analysis. Beyond the explanations extracted from the designed frameworks, relevant supporting examples as well as visualization are further provided to facilitate the interpretation. Our implemented system is demonstrated on a real-world dataset crawled from PolitiFact, where thousands of verified political news have been collected.

cs.CY

Analytic Provenance Datasets: A Data Repository of Human Analysis Activity and Interaction Logs

We present an analytic provenance data repository that can be used to study human analysis activity, thought processes, and software interaction with visual analysis tools during exploratory data analysis. We conducted a series of user studies involving exploratory data analysis scenario with textual and cyber security data. Interactions logs, think-alouds, videos and all coded data in this study are available online for research purposes. Analysis sessions are segmented in multiple sub-task steps based on user think-alouds, video and audios captured during the studies. These analytic provenance datasets can be used for research involving tools and techniques for analyzing interaction logs and analysis history. By providing high-quality coded data along with interaction logs, it is possible to compare algorithmic data processing techniques to the ground-truth records of analysis history.

cs.HC