TOWARDS A BRIGHT FUTURE: ENHANCING DIFFUSION OF CONTINUOUS CLOUD SERVICE AUDITING BY THIRD PARTIES

Using cloud services empowers organizations to achieve various financial and technical benefits. Nonetheless, customers are faced with a lack of control since they cede control over their IT resources to the cloud providers. Independent third party assessments have been recommended as good means to counteract this lack of control. However, current third party assessments fail to cope with an ever-changing cloud computing environment. We argue that continuous auditing by third parties (CATP) is required to assure continuously reliable and secure cloud services. Yet, continuous auditing has been applied mostly for internal purposes, and adoption of CATP remains lagging behind. Therefore, we examine the adoption process of CATP by building on the lenses of diffusion of innovations theory as well as conducting a scientific database search and various interviews with cloud service experts. Our findings reveal that relative advantages, a high degree of compatibility and observability of CATP would strongly enhance adoption, while a high complexity and a limited trialability might hamper diffusion. We contribute to practice and research by advancing the understanding of the CATP adop-tion process by providing a synthesis of relevant attributes that influence adoption rate. More im-portantly, we provide recommendations on how to enhance the adoption process

Trustworthy artificial intelligence

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

Why it Remains Challenging to Assess Artificial Intelligence

Artificial Intelligence (AI) assessment to mitigate risks arising from biased, unreliable, or regulatory non-compliant systems remains an open challenge for researchers, policymakers, and organizations across industries. Due to the scattered nature of research on AI across disciplines, there is a lack of overview on the challenges that need to be overcome to move AI assessment forward. In this study, we synthesize existing research on AI assessment applying a descriptive literature review. Our study reveals seven challenges along three main categories: ethical implications, regulatory gaps, and technical limitations. This study contributes to a better understanding of the challenges in AI assessment so that AI researchers and practitioners can resolve these challenges to move AI assessment forward

Why it Remains Challenging to Assess Artificial Intelligence

Artificial Intelligence (AI) assessment to mitigate risks arising from biased, unreliable, or regulatory non-compliant systems remains an open challenge for researchers, policymakers, and organizations across industries. Due to the scattered nature of research on AI across disciplines, there is a lack of overview on the challenges that need to be overcome to move AI assessment forward. In this study, we synthesize existing research on AI assessment applying a descriptive literature review. Our study reveals seven challenges along three main categories: ethical implications, regulatory gaps, and technical limitations. This study contributes to a better understanding of the challenges in AI assessment so that AI researchers and practitioners can resolve these challenges to move AI assessment forward

Understanding Interdependencies among Fog System Characteristics

Fog computing adds decentralized computing, storage, and networking capabilities with dedicated nodes as an intermediate layer between cloud data centers and edge devices to solve latency, bandwidth, and resilience issues. However, in-troducing a fog layer imposes new system design challenges. Fog systems not only exhibit a multitude of key system characteristics (e.g., security, resilience, interoperability) but are also beset with various interdependencies among their key characteristics that require developers\u27 attention. Such interdependencies can either be trade-offs with improving the fog system on one characteristic impairing it on another, or synergies with improving the system on one characteristic also improving it on another. As system developers face a multifaceted and complex set of potential system design measures, it is challenging for them to oversee all potentially resulting interdependencies, mitigate trade-offs, and foster synergies. Until now, existing literature on fog system architecture has only analyzed such interdependencies in isolation for specific characteristics, thereby limiting the applicability and generalizability of their proposed system designs if other than the considered characteristics are critical. We aim to fill this gap by conducting a literature review to (1) synthesize the most relevant characteristics of fog systems and design measures to achieve them, and (2) derive interdependences among all key characteristics. From reviewing 147 articles on fog system architectures, we reveal 11 key characteristics and 39 interdependencies. We supplement the key characteristics with a description, reason for their relevance, and related design measures derived from literature to deepen the understanding of a fog system\u27s potential and clarify semantic ambiguities. For the interdependencies, we explain and differentiate each one as positive (synergies) or negative (trade-offs), guiding practitioners and researchers in future design choices to avoid pitfalls and unleash the full potential of fog computing

What Does Not Fit Can be Made to Fit! Trade-Offs in Distributed Ledger Technology Designs

Distributed ledger technology (DLT), including blockchain, enables secure processing of transactions between untrustworthy parties in a decentralized system. However, DLT is available in different designs that exhibit diverse characteristics. Moreover, DLT characteristics have complementary and conflicting interdependencies. Hence, there will never be an ideal DLT design for all DLT use cases; instead, DLT implementations need to be configured to contextual requirements. Successful DLT configuration requires, however, a sound understanding of DLT characteristics and their interdependencies. In this manuscript, we review DLT characteristics and organize them into six groups. Furthermore, we condense interdependencies of DLT characteristics into trade-offs that should be considered for successful deployment of DLT. Finally, we consolidate our findings into DLT archetypes for common design objectives, such as security, usability, or performance. Our work makes extant DLT research more transparent and fosters understanding of interdependencies and trade-offs between DLT characteristics

Trade-offs between Distributed Ledger Technology Characteristics

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

Mind the Gap: Trade-Offs between Distributed Ledger Technology Characteristics

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 quality requirements. 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