Artificial Intelligence as a Service

  title={Artificial Intelligence as a Service},
  author={Sebastian Lins and Konstantin D. Pandl and Heiner Teigeler and Scott Thiebes and Calvin Bayer and Ali Sunyaev},
  journal={Bus. Inf. Syst. Eng.},
Artificial Intelligence (AI) is undoubtedly one of the most actively debated technologies, providing auspicious opportunities to contribute to individuals’ well-being, the success and innovativeness of organizations, and societies’ prosperity and advancement (Thiebes et al. 2020). The McKinsey Global Institute predicts that the utilization of AI could yield an additional worldwide economic output of USD 13 trillion by 2030 (Bughin et al. 2018). Organizations increasingly employ AI to perform… 

Cognitive automation

It is shown how the phenomenon of cognitive automation can be instantiated by Machine Learning-facilitated BPA systems that operate along the spectrum of lightweight and heavyweight IT implementations in larger IS ecosystems.

Understanding Interdependencies among Fog System Characteristics

A literature review of fog system architectures is conducted to synthesize the most relevant characteristics of fog systems and design measures to achieve them, and derive interdependencies among all key characteristics.



Trustworthy artificial intelligence

A data-driven research framework for TAI is developed and its utility is demonstrated by delineating fruitful avenues for future research, particularly with regard to the distributed ledger technology-based realization of TAI.

Artificial Intelligence in Service

An important implication from the theory is that analytical skills will become less important, as AI takes over more analytical tasks, giving the “softer” intuitive and empathetic skills even more importance for service employees.

A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence

This introduction to this special issue discusses artificial intelligence (AI), commonly defined as “a system’s ability to interpret external data correctly, to learn from such data, and to use those

On the Convergence of Artificial Intelligence and Distributed Ledger Technology: A Scoping Review and Future Research Agenda

This research reviews and synthesizes extant research on integrating AI with DLT and vice versa to rigorously develop a future research agenda on the convergence of both technologies, and identifies research opportunities in the areas of secure DLT, automated referee and governance, and privacy-preserving personalization.

Fair AI

There are various areas within IS that are prone to unfairness, and the objective of this article is to introduce IS practitioners and researchers to ‘‘fair AI’’.

Monitoring Misuse for Accountable 'Artificial Intelligence as a Service'

This paper introduces the concept whereby AIaaS providers uncover situations of possible service misuse by their customers, and considers the technical usage patterns that could signal situations warranting scrutiny, and raises some of the legal and technical challenges of monitoring for misuse.

Drivers and Inhibitors for Organizations' Intention to Adopt Artificial Intelligence as a Service

This research synthesizes extant research on AIaaS adoption factors and conducts semi-structured interviews with practitioners to close a gap in scholarly knowledge on the adoption of this emerging service technology, especially on inhibiting factors, and to guide future research on related behavioral and technical aspects.


Rafiki provides distributed hyper-parameter tuning for the training service, and online ensemble modeling for the inference service which trades off between latency and accuracy.


ReDCrypt is proposed, the first reconfigurable hardware-accelerated framework that empowers privacy-preserving inference of deep learning models in cloud servers and a high-throughput and power-efficient implementation of GC protocol on FPGA for the privacy-sensitive phase.

An overview on the evolution and adoption of deep learning applications used in the industry

  • S. Dutta
  • Computer Science
    WIREs Data Mining Knowl. Discov.
  • 2018
This overview traverses the evolution and successful adoption in the various industry verticals of deep learning techniques, which had lost steam during the last decade.