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.
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 tradeoffs 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.
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.
Since the emergence of blockchain in 2008, we see a kaleidoscopic variety of applications built on distributed ledger technology (DLT), including applications for financial services, healthcare, or the Internet of Things. Each application comes with specific requirements for DLT characteristics (e.g., high throughput, scalability). However, trade-offs between DLT characteristics restrict the development of a DLT design (e.g., Ethereum, IOTA) that fits all use cases' requirements. Separated DLT designs emerged, each specialized to suite dedicated application requirements. To enable the development of more powerful applications on DLT, such DLT islands must be bridged. However, knowledge of cross-chain technology (CCT) is scattered across scientific and practical sources. Therefore, we examine this diverse body of knowledge and provide comprehensive insights into CCT by synthesizing its underlying characteristics, evolving patterns, and use cases. Our findings resolve contradictions in the literature and provide avenues for future research in an emerging scientific field.
The adoption of artificial intelligence promises tremendous economic benefits for organizations. Yet, many organizations struggle to unlock the full potential of this technology. To ease the adoption of artificial intelligence for organizations, several cloud providers have begun offering artificial intelligence as a service (AIaaS). Extant research on AIaaS exhibits a strong focus on technical aspects and has opposing views on what drives or inhibits the adoption of AIaaS within organizations. In this research, we synthesize extant research on AIaaS adoption factors and conduct semi-structured interviews with practitioners. Our research yields 12 factors that drive and another 12 factors that inhibit the adoption of AIaaS in practice. We thereby close a gap in scholarly knowledge on adopting this emerging service technology, especially on inhibiting factors, and help guide future research on related behavioral and technical aspects.
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