The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences

Current smartphone operating systems regulate application permissions by prompting users on an ask-on-first-use basis. Prior research has shown that this method is ineffective because it fails to account for context: the circumstances under which an application first requests access to data may be vastly different than the circumstances under which it subsequently requests access. We performed a longitudinal 131-person field study to analyze the contextuality behind user privacy decisions to regulate access to sensitive resources. We built a classifier to make privacy decisions on the user's behalf by detecting when context has changed and, when necessary, inferring privacy preferences based on the user's past decisions and behavior. Our goal is to automatically grant appropriate resource requests without further user intervention, deny inappropriate requests, and only prompt the user when the system is uncertain of the user's preferences. We show that our approach can accurately predict users' privacy decisions 96.8% of the time, which is a four-fold reduction in error rate compared to current systems.Comment: 17 pages, 4 figure

"The teachers are confused as well": A Multiple-Stakeholder Ethics Discussion on Large Language Models in Computing Education

Large Language Models (LLMs) are advancing quickly and impacting people's lives for better or worse. In higher education, concerns have emerged such as students' misuse of LLMs and degraded education outcomes. To unpack the ethical concerns of LLMs for higher education, we conducted a case study consisting of stakeholder interviews (n=20) in higher education computer science. We found that students use several distinct mental models to interact with LLMs - LLMs serve as a tool for (a) writing, (b) coding, and (c) information retrieval, which differ somewhat in ethical considerations. Students and teachers brought up ethical issues that directly impact them, such as inaccurate LLM responses, hallucinations, biases, privacy leakage, and academic integrity issues. Participants emphasized the necessity of guidance and rules for the use of LLMs in higher education, including teaching digital literacy, rethinking education, and having cautious and contextual policies. We reflect on the ethical challenges and propose solutions

A User-centric Taxonomy for Conversational Generative Language Models

Conversational generative language models (GLMs) like ChatGPT are being rapidly adopted. Previous research on non-conversational GLMs showed that formulating prompts is critical for receiving good outputs. However, it is unclear how conversational GLMs are used when solving complex problems that require multi-step interactions. This paper addresses this research gap based on findings from a large participant event we conducted, where ChatGPT was iteratively and in a multi-step manner used while solving a complex problem. We derived a taxonomy of prompting behavior employed for solving complex problems as well as archetypes. While the taxonomy provides common knowledge on GLMs usage based on analyzed input-prompts, the different archetypes facilitate the classification of operators according to their usage. With both we provide exploratory knowledge and a foundation for design science research endeavors, which can be referred to, enabling further research and development of prompt engineering, prompting tactics, and prompting strategies on common ground

Strengthening Resilience by thinking of Knowledge as a nutrient connecting the local person to global thinking: The case of Social Technology/Tecnologia Social

In this chapter, we describe the Knowledge as a Nutrient framework that emerged from these conversations. We describe how it relates to the Tecnologia Social policy approach to sustainability, developed in Brazil (Dagnino et al. 2004, Fundação Banco do Brasil 2009, Costa 2013), which is not well known in the anglophone world. Tecnologia Social was both inspired by and rooted in Paulo Freire’s pedagogical thinking (2000, Klix 2014).   We show how this framework has the potential to increase community resilience and adaptive capacity, not only for communities that face and must adapt to climate change but for all communities in the throes of complex social, ecological, economic and political transitions.This research was supported by the International Development Research Centre, grant number IDRC GRANT NO. 106002-00

iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs

Adapting structuration theory to understand the role of reflexivity: Problematization, clinical audit and information systems

This paper is an exploratory account of the further development and application of a hybrid framework, StructurANTion, that is based on Structuration Theory and Actor Network Theory (ANT). The use of social theories in general and their use in information systems (IS) research in particular is explored leading to the use of the framework to examine the concept of what are termed humanchine networks in the context of clinical audit, within a healthcare Primary Care Trust (PCT). A particular focus is on the manner in which information systems-based reflexivity contributes to both entrenching a networks’ structurated order as well as contributing to its emancipatory change. The case study compares clinic-centric and patientcentric audit and seeks to further extend the understanding of the role of information and information systems within structurated humanchine activity systems. Conclusions indicate that the use of more socially informed IS methods and approaches can incorporate more emancipatory ideals and lead to greater adoption and usage of more relevant and useful clinical information systems and practices