@article{HermSteinbachWanneretal.2022,
  author    = {Herm, Lukas-Valentin and Steinbach, Theresa and Wanner, Jonas and Janiesch, Christian},
  title     = {A nascent design theory for explainable intelligent systems},
  series = {Electronic Markets},
  volume    = {32},
  journal   = {Electronic Markets},
  number    = {4},
  issn      = {1019-6781},
  doi       = {10.1007/s12525-022-00606-3},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-323809},
  pages     = {2185-2205},
  year      = {2022},
  abstract  = {Due to computational advances in the past decades, so-called intelligent systems can learn from increasingly complex data, analyze situations, and support users in their decision-making to address them. However, in practice, the complexity of these intelligent systems renders the user hardly able to comprehend the inherent decision logic of the underlying machine learning model. As a result, the adoption of this technology, especially for high-stake scenarios, is hampered. In this context, explainable artificial intelligence offers numerous starting points for making the inherent logic explainable to people. While research manifests the necessity for incorporating explainable artificial intelligence into intelligent systems, there is still a lack of knowledge about how to socio-technically design these systems to address acceptance barriers among different user groups. In response, we have derived and evaluated a nascent design theory for explainable intelligent systems based on a structured literature review, two qualitative expert studies, a real-world use case application, and quantitative research. Our design theory includes design requirements, design principles, and design features covering the topics of global explainability, local explainability, personalized interface design, as well as psychological/emotional factors.},
  language  = {en}
}
@article{WannerHermHeinrichetal.2022,
  author    = {Wanner, Jonas and Herm, Lukas-Valentin and Heinrich, Kai and Janiesch, Christian},
  title     = {The effect of transparency and trust on intelligent system acceptance: evidence from a user-based study},
  series = {Electronic Markets},
  volume    = {32},
  journal   = {Electronic Markets},
  number    = {4},
  issn      = {1019-6781},
  doi       = {10.1007/s12525-022-00593-5},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-323829},
  pages     = {2079-2102},
  year      = {2022},
  abstract  = {Contemporary decision support systems are increasingly relying on artificial intelligence technology such as machine learning algorithms to form intelligent systems. These systems have human-like decision capacity for selected applications based on a decision rationale which cannot be looked-up conveniently and constitutes a black box. As a consequence, acceptance by end-users remains somewhat hesitant. While lacking transparency has been said to hinder trust and enforce aversion towards these systems, studies that connect user trust to transparency and subsequently acceptance are scarce. In response, our research is concerned with the development of a theoretical model that explains end-user acceptance of intelligent systems. We utilize the unified theory of acceptance and use in information technology as well as explanation theory and related theories on initial trust and user trust in information systems. The proposed model is tested in an industrial maintenance workplace scenario using maintenance experts as participants to represent the user group. Results show that acceptance is performance-driven at first sight. However, transparency plays an important indirect role in regulating trust and the perception of performance.},
  language  = {en}
}
@article{HermJanieschHelmetal.2023,
  author    = {Herm, Lukas-Valentin and Janiesch, Christian and Helm, Alexander and Imgrund, Florian and Hofmann, Adrian and Winkelmann, Axel},
  title     = {A framework for implementing robotic process automation projects},
  series = {Information Systems and e-Business Management},
  volume    = {21},
  journal   = {Information Systems and e-Business Management},
  number    = {1},
  issn      = {1617-9846},
  doi       = {10.1007/s10257-022-00553-8},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-323798},
  pages     = {1-35},
  year      = {2023},
  abstract  = {Robotic process automation is a disruptive technology to automate already digital yet manual tasks and subprocesses as well as whole business processes rapidly. In contrast to other process automation technologies, robotic process automation is lightweight and only accesses the presentation layer of IT systems to mimic human behavior. Due to the novelty of robotic process automation and the varying approaches when implementing the technology, there are reports that up to 50\% of robotic process automation projects fail. To tackle this issue, we use a design science research approach to develop a framework for the implementation of robotic process automation projects. We analyzed 35 reports on real-life projects to derive a preliminary sequential model. Then, we performed multiple expert interviews and workshops to validate and refine our model. The result is a framework with variable stages that offers guidelines with enough flexibility to be applicable in complex and heterogeneous corporate environments as well as for small and medium-sized companies. It is structured by the three phases of initialization, implementation, and scaling. They comprise eleven stages relevant during a project and as a continuous cycle spanning individual projects. Together they structure how to manage knowledge and support processes for the execution of robotic process automation implementation projects.},
  language  = {en}
}
@article{HermJanieschFuchs2022,
  author    = {Herm, Lukas-Valentin and Janiesch, Christian and Fuchs, Patrick},
  title     = {Der Einfluss von menschlichen Denkmustern auf k{\"u}nstliche Intelligenz - eine strukturierte Untersuchung von kognitiven Verzerrungen},
  series = {HMD Praxis der Wirtschaftsinformatik},
  volume    = {59},
  journal   = {HMD Praxis der Wirtschaftsinformatik},
  number    = {2},
  issn      = {1436-3011},
  doi       = {10.1365/s40702-022-00844-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-323787},
  pages     = {556-571},
  year      = {2022},
  abstract  = {K{\"u}nstliche Intelligenz (KI) dringt vermehrt in sensible Bereiche des allt{\"a}glichen menschlichen Lebens ein. Es werden nicht mehr nur noch einfache Entscheidungen durch intelligente Systeme getroffen, sondern zunehmend auch komplexe Entscheidungen. So entscheiden z. B. intelligente Systeme, ob Bewerber in ein Unternehmen eingestellt werden sollen oder nicht. Oftmals kann die zugrundeliegende Entscheidungsfindung nur schwer nachvollzogen werden und ungerechtfertigte Entscheidungen k{\"o}nnen dadurch unerkannt bleiben, weshalb die Implementierung einer solchen KI auch h{\"a}ufig als sogenannte Blackbox bezeichnet wird. Folglich steigt die Bedrohung, durch unfaire und diskriminierende Entscheidungen einer KI benachteiligt behandelt zu werden. Resultieren diese Verzerrungen aus menschlichen Handlungen und Denkmustern spricht man von einer kognitiven Verzerrung oder einem kognitiven Bias. Aufgrund der Neuigkeit dieser Thematik ist jedoch bisher nicht ersichtlich, welche verschiedenen kognitiven Bias innerhalb eines KI-Projektes auftreten k{\"o}nnen. Ziel dieses Beitrages ist es, anhand einer strukturierten Literaturanalyse, eine gesamtheitliche Darstellung zu erm{\"o}glichen. Die gewonnenen Erkenntnisse werden anhand des in der Praxis weit verbreiten Cross-Industry Standard Process for Data Mining (CRISP-DM) Modell aufgearbeitet und klassifiziert. Diese Betrachtung zeigt, dass der menschliche Einfluss auf eine KI in jeder Entwicklungsphase des Modells gegeben ist und es daher wichtig ist „mensch-{\"a}hnlichen" Bias in einer KI explizit zu untersuchen.},
  language  = {de}
}