Algorithmic systems are increasingly deployed to make decisions that people used to make. Perceptions of these systems can significantly influence their adoption, yet, broadly speaking, users’ understanding of the internal working of these systems is limited. To explore users’ perceptions of algorithmic systems, we developed a prototype e-recruitment system called Algorithm Playground where we offer the users a look behind the scenes of such systems, and provide “how” and “why” explanations on how job applicants are ranked by their algorithms. Using an online study with 110 participants, we measured perceived fairness, transparency and trustworthiness of e-recruitment systems. Our results show that user understanding of the data and reasoning behind candidates’ rankings and selection evoked some positive attitudes as participants rated our platform to be fairer, more reliable, transparent, and trustworthy than the e-recruitment systems they have used in the past.

People are interacting with online systems all the time. In order to use the services being provided, they give consent for their data to be collected. This approach requires too much human effort and is impractical for systems like Internet-of-Things (IoT) where human-device interactions can be large. Ideally, privacy assistants can help humans make privacy decisions while working in collaboration with them. In our work, we focus on the identification and representation of privacy requirements in IoT to help privacy assistants better understand their environment. In recent years, more focus has been on the technical aspects of privacy. However, the dynamic nature of privacy also requires a representation of social aspects (e.g., social trust). In this survey paper, we review the privacy requirements represented in existing IoT ontologies. We discuss how to extend these ontologies with new requirements to better capture privacy, and we introduce case studies to demonstrate the applicability of the novel requirements.

This paper presents a case application of a socio-technical framework for Internet of Things (IoT) research and development in the United Kingdom. Applying a socio-technical system approach to IoT, this paper seeks to provide a clear understanding of the interplay between technical and non-technical aspects of IoT research and development. It describes the socio-technical requirements for IoT design and development and provides the current snapshot of research in the United Kingdom to meet these requirements. Finally, the paper provides useful information on how to conceptualize IoT research within human-centered contexts and a useful guide for centre design and evaluation to those developing new research centres or seeking to reinvigorate existing ones.

This systematic literature review explores the scholarly debate around public perceptions and behaviors in the context of cybersecurity in connected places. It reveals that, while many articles highlight the importance of public perceptions and behaviors during a cyberattack, there is no unified consensus on how to influence them in order to minimize the attack’s impact and expedite recovery. Public perceptions can affect the success and sustainability of connected places; however, exactly how and to what extent remains unknown. We argue that more research is needed on the mechanisms to assess the influence of public perceptions and associated behaviors on threats to security in connected places. Furthermore, there is a need to investigate the models and tools currently being deployed by connected place design and management to understand and influence public perceptions and behaviors. Lastly, we identify the requirements to investigate the complex relationship between the public and connected place managers, define all stakeholders clearly, and explore the patterns between specific connected place cybersecurity incidents and the methods used to transform public perceptions.