Abstract
Public attention to news has fallen by 15-20% since 2016, reflecting a shift in how the public consumes information on current events. 54% of US adults say they come across news instead of looking for it. Part of this shift has involved movement away from traditional news outlets toward social media platforms. Here, people encounter news through algorithmically curated feeds, and short-form content, increasing exposure to misinformation and sensationalism. Overall, this reduces public awareness, civic participation, critical reading skills, and can cause anxiety. Several news aggregation and summarization services exist, but these must be intentionally checked, can still be long to read, are rarely engaging, and have limited information on obscure topics. To address these issues, NewsDash was developed as an SMS news delivery service that sends short, periodic summaries about custom topics. SMS is the most direct way to get news to a user and requires minimal setup. Users are able to choose to receive and read news consistently about topics that they decide. Further, this service creates engagement, sending news in the tone of a friend and facilitating responses and engaging conversations. News consumption shapes the public’s understanding of the world and their opinions. It is essential to understand the existence and effects of bias and misinformation in a news delivery service like this. Receiving news constantly has also been linked to negative effects such as depression and anxiety. In a survey of 266 therapists, 99.6% agreed that watching or reading news can have a negative effect on mental health. These effects must be considered to create a product that has a positive impact and is adopted by users.
The STS research project investigates how computer vision automation impacts labor dynamics in industrial settings through Labor Process Theory. This theory studies the organization of work and labor control, viewing labor as a capacity requiring managerial oversight for productivity. It delves into the relationship between labor and management, viewing technology as a tool for managerial control. The project applies a quantitative literature synthesis to analyze sentiments of existing literature on industrial automation, machine vision, surveillance, and labor. By performing a thematic analysis for the themes of labor, power, and skill through the lens of labor process theory, the research examines how automation reshapes power, skill valuation, and worker experience.It found that computer vision technology for industrial automation and algorithmic worker management shifts power further towards those already in control. Employers who hold the power to deploy these technologies for increased production are able to decide what skills are valuable and control employees at scale with little effort.
Both the capstone project and STS project investigate how technology shapes and redistributes power in society. The capstone project uses technology to distribute news more directly. Power exists in that the app can choose which news to send to the user, influencing their opinions. The spread of information also plays a part in power and this tool can lower the barrier to receiving news. The STS project is based on understanding how power changes through partial automation. Both examine who technological systems serve.
