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Auflistung nach Schlagwort "CRISP-DM"

1 - 4 von 4
  • Zeitschriftenartikel
    Identification of User Roles in Enterprise Social Networks: Method Development and Application
    (Business & Information Systems Engineering: Vol. 63, No. 4, 2021) Hacker, Janine; Riemer, Kai
    The importance of gaining insights into informal organizational structures for management purposes is acknowledged by both research and practice. However, “traditional?? approaches to analyzing informal organizational social networks involve significant manual effort and do not scale for larger datasets. Enterprise Social Networks (ESN) have emerged as important tools for informal employee interactions, such as for problem-solving and information sharing. While the analysis of ESN back end data might provide insights into the informal fabric of organizations, and in particular employees’ roles in such networks, there is a lack of systematic approaches for carrying out ESN analytics, such as for user role identification. Following a design science research process, a process-based method to identify user roles from ESN data was developed and evaluated. The method’s efficacy is demonstrated through an in-depth application in a case study of Australian professional services firm Deloitte. In doing so the paper shows how ESN data can be utilized to derive metrics that characterize participation behavior, message content, and structural network positions of ESN users.
  • Zeitschriftenartikel
    Non-Discrimination-by-Design: Handlungsempfehlungen für die Entwicklung von vertrauenswürdigen KI-Services
    (HMD Praxis der Wirtschaftsinformatik: Vol. 59, No. 2, 2022) Rebstadt, Jonas; Kortum, Henrik; Gravemeier, Laura Sophie; Eberhardt, Birgid; Thomas, Oliver
    Neben der menschen-induzierten Diskriminierung von Gruppen oder Einzelpersonen haben in der jüngeren Vergangenheit auch immer mehr KI-Systeme diskriminierendes Verhalten gezeigt. Beispiele hierfür sind KI-Systeme im Recruiting, die Kandidatinnen benachteiligen, Chatbots mit rassistischen Tendenzen, oder die in autonomen Fahrzeugen eingesetzte Objekterkennung, welche schwarze Menschen schlechter als weiße Menschen erkennt. Das Verhalten der KI-Systeme entsteht hierbei durch die absichtliche oder unabsichtliche Reproduktion von Vorurteilen in den genutzten Daten oder den Entwicklerteams. Da sich KI-Systeme zunehmend als integraler Bestandteil sowohl privater als auch wirtschaftlicher Lebensbereiche etablieren, müssen sich Wissenschaft und Praxis mit den ethischen Rahmenbedingungen für deren Einsatz auseinandersetzen. Daher soll im Kontext dieser Arbeit ein wirtschaftlich und wissenschaftlich relevanter Beitrag zu diesem Diskurs geleistet werden, wobei am Beispiel des Ökosystems Smart Living auf einen sehr privaten Bezug zu einer diversen Bevölkerung bezuggenommen wird. Im Rahmen der Arbeit wurden sowohl in der Literatur als auch durch Expertenbefragungen Anforderungen an KI-Systeme im Smart-Living-Ökosystem in Bezug auf Diskriminierungsfreiheit erhoben, um Handlungsempfehlungen für die Entwicklung von KI-Services abzuleiten. Die Handlungsempfehlungen sollen vor allem Praktiker dabei unterstützen, ihr Vorgehen zur Entwicklung von KI-Systemen um ethische Faktoren zu ergänzen und so die Entwicklung nicht-diskriminierender KI-Services voranzutreiben. In addition to human-induced discrimination of groups or individuals, more and more AI systems have also shown discriminatory behavior in the recent past. Examples include AI systems in recruiting that discriminate against female candidates, chatbots with racist tendencies, or the object recognition used in autonomous vehicles that shows a worse performance in recognizing black than white people. The behavior of AI systems here arises from the intentional or unintentional reproduction of pre-existing biases in the training data, but also the development teams. As AI systems increasingly establish themselves as an integral part of both private and economic spheres of life, science and practice must address the ethical framework for their use. Therefore, in the context of this work, an economically and scientifically relevant contribution to this discourse will be made, using the example of the Smart Living ecosystem to argue with a very private reference to a diverse population. In this paper, requirements for AI systems in the Smart Living ecosystem with respect to non-discrimination were collected both in the literature and through expert interviews in order to derive recommendations for action for the development of AI services. The recommendations for action are primarily intended to support practitioners in adding ethical factors to their procedural models for the development of AI systems, thus advancing the development of non-discriminatory AI services.
  • Konferenzbeitrag
    On Characteristics and Process Requirements of Artificial Intelligence Projects
    (Projektmanagement und Vorgehensmodelle 2024 - Neues Arbeiten in Projekten – Teamarbeit neu interpretiert, 2024) Krieg, Alexander; Theobald, Sven; Brandt, Sarah; Guckenbiehl, Pascal
    For computer scientists around the world, Artificial Intelligence (AI) is not a new topic. Most people use AI unconsciously in their everyday life, for example when they use their smartphones. Since the release of ChatGPT in 2022, AI hit the interest of industry and society. The expectations on AI projects are high, companies hope to reduce costs and increase efficiency by inventing AI solutions in their companies. The aim of this paper was to better understand the characteristics of AI projects as well as the differences to classic software projects. We also wanted to investigate what kind of process model and project management framework is most suitable to successfully lead AI projects. We conducted structured interviews with four AI experts to gain an initial overview. The results provide insights into AI projects and suggest ideas for future research on software engineering for AI projects.
  • Konferenzbeitrag
    Privacy Aware Processing
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Eleks, Marian; Rebstadt, Jonas; Kortum, Henrik; Thomas, Oliver
    In many machine learning (ML) applications, the provision of data and the training as well as the analysis of machine learning systems are performed by distinct actors, a data owner and a data consumer. To protect sensitive information in these ML-scenarios, privacy aware machine learning (PAML) methods are often applied to the data before sharing. Based on the type of PAML methods used, data understanding and preparation as defined in the CRISP-DM model become more difficult if not impossible. To enable these steps, we propose a method to share a variety of uncritical information with the data consumer who is then able to define the necessary processing steps on a meta-level. These are then applied to the data in the data owners local trusted environment before the PAML-methods whereupon the prepared and protected data is shared.