56 research outputs found
Advanced Digital Auditing
This open access book discusses the most modern approach to auditing complex digital systems and technologies. It combines proven auditing approaches, advanced programming techniques and complex application areas, and covers the latest findings on theory and practice in this rapidly developing field. Especially for those who want to learn more about novel approaches to testing complex information systems and related technologies, such as blockchain and self-learning systems, the book will be a valuable resource. It is aimed at students and practitioners who are interested in contemporary technology and managerial implications
Advanced Digital Auditing
This open access book discusses the most modern approach to auditing complex digital systems and technologies. It combines proven auditing approaches, advanced programming techniques and complex application areas, and covers the latest findings on theory and practice in this rapidly developing field. Especially for those who want to learn more about novel approaches to testing complex information systems and related technologies, such as blockchain and self-learning systems, the book will be a valuable resource. It is aimed at students and practitioners who are interested in contemporary technology and managerial implications
Multikonferenz Wirtschaftsinformatik (MKWI) 2016: Technische Universität Ilmenau, 09. - 11. März 2016; Band II
Ăśbersicht der Teilkonferenzen Band II
• eHealth as a Service – Innovationen für Prävention, Versorgung und Forschung
• Einsatz von Unternehmenssoftware in der Lehre
• Energieinformatik, Erneuerbare Energien und Neue Mobilität
• Hedonische Informationssysteme
• IKT-gestütztes betriebliches Umwelt- und Nachhaltigkeitsmanagement
• Informationssysteme in der Finanzwirtschaft
• IT- und Software-Produktmanagement in Internet-of-Things-basierten Infrastrukturen
• IT-Beratung im Kontext digitaler Transformation
• IT-Sicherheit für Kritische Infrastrukturen
• Modellierung betrieblicher Informationssysteme – Konzeptuelle Modelle im Zeitalter der digitalisierten Wirtschaft (d!conomy)
• Prescriptive Analytics in I
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications
There has been a growing interest in model-agnostic methods that can make
deep learning models more transparent and explainable to a user. Some
researchers recently argued that for a machine to achieve a certain degree of
human-level explainability, this machine needs to provide human causally
understandable explanations, also known as causability. A specific class of
algorithms that have the potential to provide causability are counterfactuals.
This paper presents an in-depth systematic review of the diverse existing body
of literature on counterfactuals and causability for explainable artificial
intelligence. We performed an LDA topic modelling analysis under a PRISMA
framework to find the most relevant literature articles. This analysis resulted
in a novel taxonomy that considers the grounding theories of the surveyed
algorithms, together with their underlying properties and applications in
real-world data. This research suggests that current model-agnostic
counterfactual algorithms for explainable AI are not grounded on a causal
theoretical formalism and, consequently, cannot promote causability to a human
decision-maker. Our findings suggest that the explanations derived from major
algorithms in the literature provide spurious correlations rather than
cause/effects relationships, leading to sub-optimal, erroneous or even biased
explanations. This paper also advances the literature with new directions and
challenges on promoting causability in model-agnostic approaches for
explainable artificial intelligence
Robo-Advisory and Decision Inertia - Experimental Studies of Human Behaviour in Economic Decision-Making
Investing in the stock market is a complicated and risky undertaking for private households. In particular, private investors face numerous decisions: for instance, whether to invest in stocks
or bonds, buy passively or actively managed investment products, or try something new like Bitcoin. They must decide where they can get independent financial advice, and whether this advice is trustworthy.
As a consequence, information systems researchers design and build financial decision support systems. Robo-advisors are such decision support systems aiming to provide independent advice, and support private households in investment decisions and wealth management. This thesis evaluates robo-advisors, their design and use and thus their ability to support financial decision-making. Addressing this research need, my thesis is organized in three parts (part I-III ) consisting of four quantitative experimental studies, two qualitative friendly-user-studies, and one qualitative interview study.
In Part I, Chapter 3 examines how robo-advisors can be designed for inexperienced investors. In particular, I derive design recommendations for the development of robo-advisor solutions and evaluate them in a three-cycle design sciences process. Requirements related to the clusters ease of interaction, work efficiency, information processing and cognitive load are identified as key elements for robo-advisory design.
In Part II, Chapter 4 focuses on an important bias in economic decision-making - decision inertia, the tendency to repeat a decision regardless of the consequences. As a result, a decision-maker can make repeated suboptimal investments. To understand this bias more deeply, I investigate decision inertia in a general experimental setting and identify motivational and cognitive drivers of this phenomenon. Thus, I relied on behavioural, on self-reported, and on bio-physiological measures in three laboratory studies.
In Part III, Chapter 5 specifies the findings from Part II to find and evaluate strategies to reduce decision inertia in financial decision support systems. For that purpose, I investigate two nudges (design features) to reduce inertia in investment decisions. My results suggest that defaults and warning messages can help participants to overcome decision inertia. Furthermore, the results illustrate that designers have to be careful not to push decision-makers into the decision inertia bias by accident.
In summary, this thesis gives design recommendations for practitioners and scholars building robo-advisors. The insights can help to develop robo-advisors, and to increase advisor quality by considering decision inertia in the system design phase and consequently, it illustrates how to counteract this malicious decision bias for private investors
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