With the increasing dominance of internet as a source of news consumption, there has been a rise in the production and popularity of email newsletters compiled by individual journalists. However, there is little research on the processes of aggregation, and how these differ between expert journalists and trained machines. In this paper, we interviewed journalists who curate newsletters from around the world. Through an in-depth understanding of journalists’ workflows, our findings lay out the role of their prior experience in the value they bring into the curation process, their own use of algorithms in finding stories for their newsletter, and their internalization of their readers’ interests and the context they are curating for. While identifying the role of human expertise, we highlight the importance of hybrid curation and provide design insights on how technology can support the work of these experts.
University students are well known for volunteering within non-governmental organisations (NGOs).
A significant part of NGO practice is the production of documents that communicate their work to local communities and international stakeholders.
However, organisations often struggle to resource translations of these documents, resulting in the exclusion of the very same communities that they want to reach.
Although many students are multilingual and are willing to volunteer their time and language skills, there are few structured opportunities configured for such non-professional translation of content in the short-term mode that would fit into the student pattern of availability.
We developed Action Translate to specifically support these motivated, non-professional translators within the volunteering constraints of university life.
Action Translate leverages machine translation post-editing to support teams of volunteers working on NGO translation projects online.
Through analysis of a real-world deployment, we discuss how digital systems can be developed to better support student volunteer translators, specifically in building collegiate interaction and identity as translators for a cause.
AI-based design tools are proliferating in professional software to assist engineering and industrial designers in complex manufacturing and design tasks. These tools take on more agentic roles than traditional computer-aided design tools and are often portrayed as “co-creators.” Yet, working effectively with such systems requires different skills than working with complex CAD tools alone. To date, we know little about how engineering designers learn to work with AI-based design tools. In this study, we observed trained designers as they learned to work with two AI-based tools on a realistic design task. We find that designers face many challenges in learning to effectively co-create with current systems, including challenges in understanding and adjusting AI outputs and in communicating their design goals. Based on our findings, we highlight several design opportunities to better support designer-AI co-creation.
This paper explores how the design of interactive voice assistants (IVAs) might be tailored to support home health aides' important work in complex home care contexts. We designed two custom IVAs: one that looks like an aide's medical kit and one that blends into the home environment. We also designed a voice-based application that provides aides with guidance for day-to-day tasks and for performing a medical assessment. Via a lab-based study with 25 aides and seven patients, we explore how tailoring the IVAs' design to home health care might impact its acceptability as a work device, enabling cooperative work among aides and clients, while potentially causing conflict that will require IVA designers to decide whose values to prioritize. We also highlight limits in aides' power to control IVAs in clients' homes. Finally, we discuss implications for designing privacy-preserving IVAs, including leveraging IVAs' physical design to enact privacy mechanisms and opportunities to build `always on' IVAs for privacy-sensitive contexts like home health care.
The use of algorithms for decision-making in higher education is steadily growing, promising cost-savings to institutions and personalized service for students but also raising ethical challenges around surveillance, fairness, and interpretation of data. To address the lack of systematic understanding of how these algorithms are currently designed, we reviewed an extensive corpus of papers proposing algorithms for decision-making in higher education. We categorized them based on input data, computational method, and target outcome, and then investigated the interrelations of these factors with the application of human-centered lenses: theoretical, participatory, or speculative design. We found that the models are trending towards deep learning, and increased use of student personal data and protected attributes, with the target scope expanding towards automated decisions. However, despite the associated decrease in interpretability and explainability, current development predominantly fails to incorporate human-centered lenses. We discuss the challenges with these trends and advocate for a human-centered approach.
News media often leverage documents to find ideas for stories, while being critical of the frames and narratives present. Developing angles from a document such as a press release is a cognitively taxing process, in which journalists critically examine the implicit meaning of its claims. Informed by interviews with journalists, we developed AngleKindling, an interactive tool which employs the common sense reasoning of large language models to help journalists explore angles for reporting on a press release. In a study with 12 professional journalists, we show that participants found AngleKindling significantly more helpful and less mentally demanding to use for brainstorming ideas, compared to a prior journalistic angle ideation tool. AngleKindling helped journalists deeply engage with the press release and recognize angles that were useful for multiple types of stories. From our findings, we discuss how to help journalists customize and identify promising angles, and extending AngleKindling to other knowledge-work domains.