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

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

Owner-Level Taxes and Business Activity

In some classes of models, taxes at the owner level are "neutral" and have no effect on firm activity. However, this tax neutrality is sensitive to assumptions and no longer holds in more complex models. We review recent research that incorporates greater complexity in studying the link between taxes and business activity - particularly entrepreneurship. Dividend taxes on owners of large firms affect firm activity in models that include agency conflicts between owners and managers. Similarly, after incorporating entrepreneurs' occupational choice into the model, taxes are no longer neutral. By forsaking lucrative alternative careers, skilled entrepreneurs tend to have high opportunity costs, which make the choice of attempting to start a business of first order importance. Moreover, in models where it is assumed that capital flows across borders without cost, taxes on domestic business owners do not alter business activity because foreign capital seamlessly compensates for tax-induced declines in investments. This theoretical notion is contradicted by the strong "home bias" observed in business ownership, in particular for small firms and startups without easy access to international capital markets. Recent empirical work has emphasized that taxes have heterogeneous effects on mature firms, entrepreneurial startups, and owner-managed small firms. Lowering dividend taxes on firms with dispersed ownership has been shown to shift capital from mature firms into rapidly growing firms. Moreover, capital gains taxation tends to reduce the number of innovative startups and diminish venture capital activity, while high owner-level taxes encourage small business activity and non-entrepreneurial self-employment because such firms have more opportunities to avoid or evade taxes. To obtain efficient incentives in entrepreneurial startups, contractual terms are required that ex ante guarantee that all providers of critical inputs, especially equity constrained entrepreneurs, are entitled to a share of the resulting capital value firm. Unless properly designed, owner-level taxes prevent such ex ante contracting and thus lower the likelihood of eventual success

Human-centered AI Systems Engineering: Konzeption und Implementierung von KI-Systemen in menschzentrierten Datenökosystemen

Die Entwicklung von KI-Systemen erfolgt oft innerhalb komplexer Datenökosysteme, in denen verschiedene Akteure miteinander interagieren und Daten austauschen. Eine solche akteursübergreifende Entwicklung birgt großes Potenzial, aber auch Herausforderungen, insbesondere wenn der Mensch als zentraler Akteur und Anwender der KI-Systeme im Mittelpunkt steht. In diesem Spannungsfeld nehmen Aspekte wie Nicht-Diskriminierung, Erklärbarkeit, Transparenz und Vertrauen einen hohen Stellenwert ein. KI-Systeme sollten daher auf den Menschen ausgerichtet sein, um ethische Standards zu erfüllen, nicht zuletzt auch, um einer zunehmenden Regulierung durch die Europäische Union zu begegnen, welche die Gestaltung und Nutzung von KI-Technologien in Europa maßgeblich beeinflussen wird. Aus den zuvor skizzierten Sachverhalten erwächst die Fragestellung, wie KI-Systeme in menschzentrierten Datenökosystemen konzipiert, entwickelt und eingesetzt werden können, um einerseits Mehrwerte für die Gesamtheit einer pluralistischen Gesellschaft zu erzielen, andererseits aber auch, um den Bedürfnissen und Rechten jedes Einzelnen Genüge zu tun. Diese Dissertation liefert eine Antwort auf diese Frage, indem sie das Engineering von KI-Systemen in Datenökosystemen untersucht und Ansätze zu deren menschzentrierter Gestaltung hervorbringt

Data-based Customer-Retention-as-a-Service: Inductive Development of a Data-Based Business Model Based on a Case Study of the Automotive Industry

Viele Unternehmen setzen Künstliche Intelligenz zur Verarbeitung großer Datenmengen bereits heute erfolgreich für die Kundenbindung ein. So schaffen große Unternehmen individuelle Kundenerlebnisse basierend auf der Auswertung großer kundenbezogener Datenmengen zur kurz- aber auch langfristigen Kundenbindung, z. B. durch intelligente Empfehlungen von Inhalten auf Videoplattformen. Bei Unternehmen mit traditioneller Wertschöpfung wird dieses Potenzial jedoch noch nicht ausreichend genutzt. Vor diesem Hintergrund wird im Rahmen einer Fallstudie exemplarisch ein datengetriebenes Kundenbindungsszenario in Kooperation mit einer Autowerkstatt umgesetzt. Im konkreten Fall wurde eine zeitlich optimierte Kundenansprache auf Basis von KI-basierten Prognosen der täglichen Fahrleistung von Kunden angestrebt. Grundlage dafür war die Analyse eines Kundendatensatzes einer Autowerkstatt und die anschließende Entwicklung einer Künstlichen Intelligenz. Aufbauend auf der Fallstudie wird ein datenbasiertes Geschäftsmodell konzipiert, dessen Werteangebot vor allem Unternehmen mit traditioneller Wertschöpfung und wenig Wissen im Bereich Künstlicher Intelligenz dazu befähigt, datenbasierte Technologien in der Kundenbindung einzusetzen. Das dem Geschäftsmodell zugrundeliegende Plattformkonzept wird dabei als Open-Innovation-Modell entwickelt und soll neben der Entwicklung eigener Services auch die Interaktion von Datenkonsumenten, Datenlieferanten und anderen Datenbefähigern, mit dem Ziel sich als Datenökosystem für Kundenbindung zu etablieren, unterstützen