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Abstract—SALUS is an open-source security system that uses inexpensive, affordable hardware, specifically catering towards primary and secondary schools. Students, teachers, administrators, and parents have endured the reality of school shootings. SALUS’ intent is to alleviate gun violence on school campuses and keep the institutions safe. SALUS is easily activated and managed through a graphical user interface, and the system autonomously contacts faculty, administrators, and local law enforcement if a gun is detected on camera. The system was also designed to adhere to federal FERPA laws. The user-interface is created using QT creator, and the physical working prototype is made using a Raspberry Pi 4 and a Dorhea Raspberry Pi Camera. The Raspberry Pi uses UDP(User Datagram Protocol) to send images from the camera to the desktop server which detects motion using functions from the OpenCV library. Using YOLOv3 and Google Research’s Colab, motion detected images are processed and searched for weapons. If a weapon is detected in the images, Twilio is used to send text messages and SMTP protocols are used to send emails from Google’s email service Gmail to the contacts listed on the GUI. The working prototype has achieved an accuracy of 80% or greater in non-testing conditions. The best recorded time the system took to notify those on the contacts list was 8.97 seconds. Thus, our system successfully decreases tactical time and notification time, while maximizing response time to save more lives overall.

Taking the technology-related discourse in the Chinese context as research object, combining automated content analysis, survey, and in-depth interview, this study explores the main discourse frames that Chinese news outlets and social media incorporated to report data-driven technologies and how Chinese technology users perceive and evaluate the cur rent technological trends. The preliminary study discovered that the technological discourse of the Chinese mainstream media were positive in general, which differed a lot from the negative technological discourses in western countries. Though most of Chinese published positive attitude toward technologies, they also showed worries about potential technological risks. We argue that different political cultures and contexts for technology use are likely reasons for these differences between China and western countries.

Digital media serve manifold purposes connected to communication, transition, resource allocation, and integration in the context of forced migration. Many apps and platforms rely on user data. Organizations do not always clearly communicate what data are collected for which purposes. Policies on data and privacy should convey this information, but they rarely increase clarity and transparency. This can create vulnerabilities for users and raises questions about data literacy: How much do they understand about data practices and how can organizations inform them better? This qualitative study explores 10 digital services (apps-based and web-based) with the walkthrough method in combination with a content analysis of data policies. It charts communication efforts about data practices and consequences for privacy. The findings imply that many organizations fail to address these issues efficiently. They need to critically revise their communication strategies to create transparency and build data literacy among users.

The user experience of our daily interactions is increasingly shaped with the aid of AI, mostly as the output of recommendation engines. However, it is less common to present users with possibilities to navigate or adapt such output. In this paper we argue that adding such algorithmic controls can be a potent strategy to create explainable AI and to aid users in building adequate mental models of the system. We describe our efforts to create a pattern library for algorithmic controls: the algorithmic affordances pattern library. The library can aid in bridging research efforts to explore and evaluate algorithmic controls and emerging practices in commercial applications, therewith scaffolding a more evidence-based adoption of algorithmic controls in industry. A first version of the library suggested four distinct categories of algorithmic controls: feeding the algorithm, tuning algorithmic parameters, activating recommendation contexts, and navigating the recommendation space. In this paper we discuss these and reflect on how each of them could aid explainability. Based on this reflection, we unfold a sketch for a future research agenda. The paper also serves as an open invitation to the XAI community to strengthen our approach with things we missed so far.

This study explores the news framing of A.I., China, and the U.S.A. in two mainstream news outlets: The Washington Post and The South China Morning Post. The main objective is to analyse how both, as parts of different discourse cultures, portray the competition for leadership in A.I. innovation between China and the U.S.A. The study takes a critical look at the media discourse on the ‘new arms race’ and what role the news play in localising global technology trends. The empirical part makes use of a combination of manual and automated content analyses. To this end, a dictionary approach that utilises Names Entity Recognition was applied to a large volume of news texts ( N = 3.055) to identify recurring news frames. The findings show similarities in the perception of A.I.’s potentials and versatility but also clear cultural differences in how risks and conflicts are portrayed in both outlets. Although the Washington Post appears more critical about A.I., the South China Morning Post frames the technology as a driver of economic growth and global influence. The discussion offers a starting point for theorising the relationship between mediatisation, tech trends, cultural differences, and international politics.