Editorial: Special Issue on Data Transparency—Uses Cases and Applications

Advances in Artificial Intelligence (AI) and mobile and Internet technologies have been progressively reshaping our lives over the past few years. The applications of the Internet of Things and cyber-physical systems today touch almost all aspects of our daily lives, including healthcare (e.g., remote patient monitoring environments), leisure (e.g., smart entertainment spaces), and work (e.g., smart manufacturing and asset management). For many of us, social media have become the rule rather than the exception as the way to interact, socialize, and exchange information. AI-powered systems have become a reality and started to affect our lives in important ways. These systems and services collect huge amounts of data about us and exploit it for various purposes that could affect our lives positively or negatively. Even though most of these systems claim to abide by data protection regulations and ethics, data misuse incidents keep making the headlines. In this new digital world, data transparency for end users is becoming a fundamental aspect to consider when designing, implementing, and deploying a system, service, or software [1, 3, 4]. Transparency allows users to track down and follow how their data are collected, transmitted, stored, processed, exploited, and serviced. It also allows them to verify how fairly they are treated by algorithms, software, and systems that affect their lives. Data transparency is a complex concept that is interpreted and approached in different ways by different research communities and bodies. A comprehensive definition of data transparency is proposed by Bertino et al. as “the ability of subjects to effectively gain access to all information related to data used in processes and decisions that affect the subjects” [2].