Dissection of AI Job Advertisements: A Text Mining-based Analysis of Employee Skills in the Disciplines Computer Vision and Natural Language Processing

Human capital is a well discussed topic in information system research. In order for companies to develop and use IT artifacts, they need specialized employees. This is especially the case when complex technologies, such as artificial intelligence, are used. Two major fields of artificial intelligence are computer vision (CV) and natural language processing (NLP). In this paper skills and know-how required for CV and NLP specialists are analyzed and compared from a job market perspective. For this purpose, we utilize a text mining-based analysis pipeline to dissect job advertisements for artificial intelligence. In concrete, job advertisements of both sub-disciplines were crawled from a large international online job platform and analyzed using named entity recognition and term vectors. It could be shown that know-how and skills required differ between the two job profiles. There is no general requirement profile of an artificial intelligence specialist, and it requires a differentiated consideration

Integrating Data and Service Lifecycle for Smart Service Systems Engineering: Compilation of a Lifecycle Model for the Data Ecosystem of Smart Living

In smart service systems engineering, where actors rely on the mutual exchange of data to create complex and holistic solutions, integration is crucial. Nevertheless, the management of data as a driving resource still lacks organizational structure. There is no holistic lifecycle approach that integrates data and service lifecycle and adopts a cross-actor perspective. Especially in data ecosystems, where sovereign actors depend on the mutual exchange of data to create complex, but transparent service systems, an integration is of crucial importance. This particularly applies to the smart living domain, where different industries, products and services interact in a complex environment. In this paper we address this shortcoming by proposing an integrated model that covers the different relevant lifecycles based on a systematic literature review and supplement it by concrete domain requirements from the smart living ecosystem obtained through semi-structured expert interviews

Towards Personalized Explanations for AI Systems: Designing a Role Model for Explainable AI in Auditing

Due to a continuously growing repertoire of available methods and applications, Artificial Intelligence (AI) is becoming an innovation driver for most industries. In the auditing domain, initial approaches of AI have already been discussed in scientific discourse, but practical application is still lagging behind. Caused by a highly regulated environment, the explainability of AI is of particular relevance. Using semi-structured expert interviews, we identified stakeholder specific requirements regarding explainable AI (XAI) in auditing. To address the needs of all involved stakeholders a theoretical role model for AI systems has been designed based on a systematic literature review. The role model has been instantiated and evaluated in the domain of financial statement auditing using focus groups of domain experts. The resulting model offers a foundation for the development of AI systems with personalized explanations and an optimized usage of existing XAI methods

Proposing a Roadmap for Designing Non-Discriminatory ML Services: Preliminary Results from a Design Science Research Project

Artificial Intelligence (AI) and Machine Learning (ML) algorithms are being developed with ever higher accuracy. However, the use of ML also has its dark side. In the recent past, examples have repeatedly emerged of ML systems learning discriminatory and even racist or sexist patterns and acting accordingly. As ML systems become an integral part of both private and economic spheres of life, academia and practice must address the question of how non-discriminatory ML algorithms can be developed to benefit everyone. This is where our research in progress paper contributes. Using a real-world smart living case study, we investigated discrimination in terms of ethnicity and gender within state-of-the-art pre-trained ML models for face recognition and quantified it using an F1 metric. Building on these empirical findings as well as on the state of the scientific literature, we propose a roadmap for further research on the development of non-discriminatory ML services

Introducing a methodological approach to determine value shares in Digital Ecosystems

Motivated by the critical yet unsolved task of fair value distribution in digital ecosystems (DEs), this study presents a methodological approach that allows us to determine ecosystem components\u27 value share to the total co-created value. Our method takes a holistic perspective on DEs. It suggests that when viewing DEs as complex networks, the value share of a component to the total co-created value stems from the network size and the interaction between the network participants. We demonstrate the applicability of the proposed method in a simulation of a Smart Living service ecosystem. Our simulation shows that our method is suitable for unraveling hitherto hidden interconnectedness between value-co-creating ecosystem components. Components that offer a low structural contribution to the total value can still play a crucial role in the network and have the most significant value share to the whole network

Trustworthy Artificial Intelligence Systems Engineering: Konzeption und Implementierung vertrauenswürdiger KI-Systeme

In den vergangenen Jahren wurden zahlreiche Ansätze in dem Gebiet der Künstlichen Intel-ligenz (KI) entwickelt und auch in die Praxis übertragen, sodass bereits konkrete Mehrwer-te für Unternehmen, in der Forschung und für die Gesellschaft geschaffen werden. Bei der Etablierung dieser Systeme ergeben sich jedoch Herausforderungen wie eine fehlende Transparenz oder auch die Verstärkung von Diskriminierungen. Prinzipien und Konzepte Vertrauenswürdiger oder auch ethischer KI bieten hierzu zwar bereits abstrakte Ansätze, es fehlen jedoch an vielen Stellen noch Konkretisierungen und praktisch anwendbare Hand-lungsempfehlungen. Im Zuge dieser Dissertation wurde mit der Konkretisierung von drei zentralen Prinzipien Vertrauenswürdiger KI, nämlich Transparenz, Nicht-Diskriminierung und Datenschutz, ein wichtiger Schritt für die Etablierung von Vertrauenswürdigen KI-Systemen erzielt. Aufsetzend auf praktischen Umsetzungen in den Domänen Wirtschafts-prüfung und Smart Living wurde eine Grundlage für die Entwicklung von KI-Systemen in Regulatorik-geprägten Domänen gelegt, welche sich durch die im Allgemeinen stark wach-senden regulatorischen Anforderungen auch auf andere Bereiche übertragen lässt, um Mehrwerte für eine Vielzahl von Unternehmen zu schaffen

Developing an Artificial Intelligence Maturity Model for Auditing

Artificial Intelligence (AI) is increasingly being used in various domains including highly regulated areas such as auditing. Although the use of AI in auditing may seem promising at the first glance, there are a number of implications that have so far prevented its broad application. By proposing the first Auditing Artificial Intelligence Maturity Model (A-AIMM), we assess the adoption and diffusion of AI in auditing by considering audit specific requirements. The resulting model contains eight different dimensions and five different maturity levels that foster audit firms in becoming AI-enabled organisations by providing recommendations for the further use of AI with their current capabilities. The development procedure represents a Design Science Research approach including a systematic literature review, a qualitative survey with audit experts and an iterative development process