Artificial intelligence (AI) brings forth many opportunities to contribute to the wellbeing of individuals and the advancement of economies and societies, but also a variety of novel ethical, legal, social, and technological challenges. Trustworthy AI (TAI) bases on the idea that trust builds the foundation of societies, economies, and sustainable development, and that individuals, organizations, and societies will therefore only ever be able to realize the full potential of AI, if trust can be established in its development, deployment, and use. With this article we aim to introduce the concept of TAI and its five foundational principles (1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice, and (5) explicability. We further draw on these five principles to develop a data-driven research framework for TAI and demonstrate its utility by delineating fruitful avenues for future research, particularly with regard to the distributed ledger technology-based realization of TAI.

Mobile health (mHealth) customers shopping for applications (apps) should be aware of app privacy practices so they can make informed decisions about purchase and use. We sought to assess the availability, scope, and transparency of mHealth app privacy policies on iOS and Android. Over 35 000 mHealth apps are available for iOS and Android. Of the 600 most commonly used apps, only 183 (30.5%) had privacy policies. Average policy length was 1755 (SD 1301) words with a reading grade level of 16 (SD 2.9). Two thirds (66.1%) of privacy policies did not specifically address the app itself. Our findings show that currently mHealth developers often fail to provide app privacy policies. The privacy policies that are available do not make information privacy practices transparent to users, require college-level literacy, and are often not focused on the app itself. Further research is warranted to address why privacy policies are often absent, opaque, or irrelevant, and to find a remedy.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Cloud computing (CC) is an emerging form of IT outsourcing (ITO) that requires organizations to adjust their sourcing processes. Although ITO researchers have established an extensive knowledge base on the determinant factors that drive sourcing decisions from various theoretical perspectives, the majority of research on cloud-sourcing decisions focuses on technological aspects. We reviewed the CC and ITO literature and systematically coded the determinant factors that influence sourcing decisions. We show that most determinant factors of sourcing decisions in the ITO context remain valid for the CC context. However, the findings for some factors (i.e., asset specificity, client firm IT capabilities, client firm size, institutional influences, and uncertainty) are inconclusive for the CC and ITO contexts. We discuss how the peculiarities of CC can explain these inconclusive findings. Our results indicate that CC researchers should draw from research on ITO decision making but re-examine ITO concepts in the light of the peculiarities of CC, such as the differences between software and infrastructure services, the self-service procurement of cloud services, or the evolving role of IT departments. By summarizing determinant factors of cloud-sourcing decisions for consideration in future research, we contribute to the development of endogenous theories in the IS domain.

When developing peer-to-peer applications on distributed ledger technology (DLT), a crucial decision is the selection of a suitable DLT design (e.g., Ethereum), because it is hard to change the underlying DLT design post hoc. To facilitate the selection of suitable DLT designs, we review DLT characteristics and identify trade-offs between them. Furthermore, we assess how DLT designs account for these trade-offs and we develop archetypes for DLT designs that cater to specific requirements of applications on DLT. The main purpose of our article is to introduce scientific and practical audiences to the intricacies of DLT designs and to support development of viable applications on DLT.