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Auflistung nach Autor:in "Esau, Margarita"

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  • Zeitschriftenartikel
    Data Storytelling als kritischer Erfolgsfaktor von Data Science
    (HMD Praxis der Wirtschaftsinformatik: Vol. 57, No. 5, 2020) Neifer, Thomas; Lawo, Dennis; Bossauer, Paul; Esau, Margarita; Jerofejev, Anna-Maria
    Bedingt durch die fortlaufende Digitalisierung und den Big Data-Trend stehen immer mehr Daten zur Verfügung. Daraus resultieren viele Potenziale – gerade für Unternehmen. Die Fähigkeit zur Bewältigung und Auswertung dieser Daten schlägt sich in der Rolle des Data Scientist nieder, welcher aktuell einer der gefragtesten Berufe ist. Allerdings ist die Integration von Daten in Unternehmensstrategie und -kultur eine große Herausforderung. So müssen komplexe Daten und Analyseergebnisse auch nicht datenaffinen Stakeholdern kommuniziert werden. Hier kommt dem Data Storytelling eine entscheidende Rolle zu, denn um mit Daten eine Veränderung hervorrufen zu können, müssen vorerst Verständnis und Motivation für den Sachverhalt zielgruppenspezifisch geschaffen werden. Allerdings handelt es sich bei Data Storytelling noch um ein Nischenthema. Diese Arbeit leitet mithilfe einer systematischen Literaturanalyse die Erfolgsfaktoren von Data Storytelling für eine effektive und effiziente Kommunikation von Daten her, um Data Scientists in Forschung und Praxis bei der Kommunikation der Daten und Ergebnisse zu unterstützen. Due to the ongoing digitalization and the Big Data trend, an increasing amount of data is available. This results in many potentials—especially for companies. The ability to cope with and evaluate this data is reflected in the role of the data scientist, which is one of the most popular jobs at present. However, challenges arise from the integration of data into corporate strategy and culture. For example, complex data and analysis results must be communicated to stakeholders who are not data-affine. Data storytelling plays a decisive role here, because to use data to initiate change, understanding and motivation for the issue must first be created for every target group. However, data storytelling is still a niche topic. This article uses a systematic literature analysis to derive the success factors of data storytelling for an effective and efficient communication of data to support Data Scientists in research and practice in communicating data and results.
  • Zeitschriftenartikel
    Erratum zu: Data Storytelling als kritischer Erfolgsfaktor von Data Science
    (HMD Praxis der Wirtschaftsinformatik: Vol. 58, No. 4, 2021) Neifer, Thomas; Lawo, Dennis; Bossauer, Paul; Esau, Margarita; Jerofejev, Anna-Maria
  • Workshopbeitrag
    eXplainable AI: Take one Step Back, Move two Steps forward
    (Mensch und Computer 2020 - Workshopband, 2020) Alizadeh, Fatemeh; Esau, Margarita; Stevens, Gunnar; Cassens, Lena
    In 1991 the researchers at the center for the Learning Sciences of Carnegie Mellon University were confronted with the confusing question of “where is AI” from the users, who were interacting with AI but did not realize it. Three decades of research and we are still facing the same issue with the AItechnology users. In the lack of users’ awareness and mutual understanding of AI-enabled systems between designers and users, informal theories of the users about how a system works (“Folk theories”) become inevitable but can lead to misconceptions and ineffective interactions. To shape appropriate mental models of AI-based systems, explainable AI has been suggested by AI practitioners. However, a profound understanding of the current users’ perception of AI is still missing. In this study, we introduce the term “Perceived AI” as “AI defined from the perspective of its users”. We then present our preliminary results from deep-interviews with 50 AItechnology users, which provide a framework for our future research approach towards a better understanding of PAI and users’ folk theories.
  • Zeitschriftenartikel
    I Don’t Know, Is AI Also Used in Airbags? - An Empirical Study of Folk Concepts and People’s Expectations of Current and Future Artificial Intelligence
    (i-com: Vol. 20, No. 1, 2021) Alizadeh, Fatemeh; Stevens, Gunnar; Esau, Margarita
    In 1991, researchers at the center for the Learning Sciences of Carnegie Mellon University were confronted with the confusing question of “where is AI?” from users, who were interacting with artificial intelligence (AI) but did not realize it. After three decades of research, we are still facing the same issue with the unclear understanding of AI among people. The lack of mutual understanding and expectations among AI users and designers and the ineffective interactions with AI that result raises the question of “how AI is generally perceived today?” To address this gap, we conducted 50 semi-structured interviews on perception and expectations of AI. Our results revealed that for most, AI is a dazzling concept that ranges from a simple automated device up to a full controlling agent and a self-learning superpower. We explain how these folk concepts shape users’ expectations when interacting with AI and envisioning its current and future state.