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Prof. Dr. Benjamin van Giffen

Müller-Friedberg-Strasse 6/8
9000 St. Gallen
+41 71 224 3635


Algorithmic forecasts outperform human forecasts by 10% on average. State-of-the-art machine learning (ML) algorithms have further expanded this discrepancy. Because a variety of other activities rely on them, sales forecasting is critical to a company's profitability. However, individuals are hesitant to use ML forecasts. To overcome this algorithm aversion, explainable artificial intelligence (XAI) can be a solution by making ML systems more comprehensible by providing explanations. However, current XAI techniques are incomprehensible for laymen, as they impose too much cognitive load. We contribute to this research gap by investigating the effectiveness in terms of forecast accuracy of two example-based explanation approaches. We conduct an online experiment based on a two-by-two between-subjects design with factual and counterfactual examples as experimental factors. A control group has access to ML predictions, but not to explanations. We report results of this study: While factual explanations significantly improved participants' decision quality, counterfactual explanations did not.

Wissenschaftlicher Artikel
Viele der wertvollsten Unternehmen der Welt betreiben ihr Geschäft auf Basis einer digitalen Plattform samt umgebendem Ökosystem. Während es in der Theorie zahlreiche Erklärungs-und Gestaltungsansätze für die erfolgreiche Umsetzung gibt, gelten diese wirtschaftlich attraktiven Geschäftsmodelle in der Praxis nach wie vor als herausfordernd. Auf der empirischen Grundlage von sieben Plattform-Innovationsprojekten und mit Methoden der Fallstudienforschung untersucht der vorliegende Artikel, welche Rolle digitale Plattformen in der Praxis spielen und wie diese Artefakte entwickelt werden können. Mit den Ergebnissen in Form von vier Einsatz- (Platform-as-a-Core, Platform-as-an-Evolution, Platform-as-an-Enabler, Platform-as-an-Add-On) und vier Entwicklungsmodellen (Methodic Problem Solvers, Methodic Strategists, Methodic Leaders, Ad-Hoc Developers) kann gefolgert werden: Digitale Plattformen können in der Praxis vielfältige Rollen einnehmen und deren Entwicklung kann mit unterschiedlicher Methodikintensität erfolgen. Für die Praxis profitieren Fach- und Führungskräfte von industrienahen Einblicken und abgeleiteten Handlungsempfehlungen. Für die Forschung wird der Wissensfundus im Bereich des Designs und der Entwicklung digitaler Plattformen erweitert.

Algorithmic forecasts outperform human forecasts in many tasks. State-of-the-art machine learning (ML) algorithms have even widened that gap. Since sales forecasting plays a key role in business profitability, ML based sales forecasting can have significant advantages. However, individuals are resistant to use algorithmic forecasts. To overcome this algorithm aversion, explainable AI (XAI), where an explanation interface (XI) provides model predictions and explanations to the user, can help. However, current XAI techniques are incomprehensible for laymen. Despite the economic relevance of sales forecasting, there is no significant research effort towards aiding non-expert users make better decisions using ML forecasting systems by designing appropriate XI. We contribute to this research gap by designing a model-agnostic XI for laymen. We propose a design theory for XIs, instantiate our theory and report initial formative evaluation results. A real-world evaluation context is used: A medium-sized Swiss bakery chain provides past sales data and human forecasts.

Over the last decade, the importance of machine learning increased dramatically in business and marketing. However, when machine learning is used for decision-making, bias rooted in unrepresentative datasets, inadequate models, weak algorithm designs, or human stereotypes can lead to low performance and unfair decisions, resulting in financial, social, and reputational losses. This paper offers a systematic, interdisciplinary literature review of machine learning biases as well as methods to avoid and mitigate these biases. We identified eight distinct machine learning biases, summarized these biases in the cross-industry standard process for data mining to account for all phases of machine learning projects, and outline twenty-four mitigation methods. We further contextualize these biases in a real-world case study and illustrate adequate mitigation strategies. These insights synthesize the literature on machine learning biases in a concise manner and point to the importance of human judgment for machine learning algorithms.

The algorithms of artificial intelligence are constantly being further developed and are being used in more and more products and applications in business and society. Numerous prototypes are being developed to open up the use of artificial intelligence in a wide variety of application areas. Nevertheless, only a few prototypes succeed in making the leap into productive applications that create sustainable business benefits. This paper series shows that processes and structures are needed for the management of artificial intelligence to ensure the sustainable success of AI systems.

This paper introduces a framework for managing bias in machine learning (ML) projects. When ML-capabilities are used for decision making, they frequently affect the lives of many people. However, bias can lead to low model performance and misguided business decisions, resulting in fatal financial, social, and reputational impacts. This framework provides an overview of potential biases and corresponding mitigation methods for each phase of the well-established process model CRISP-DM. Eight distinct types of biases and 25 mitigation methods were identified through a literature review and allocated to six phases of the reference model in a synthesized way. Furthermore, some biases are mitigated in different phases as they occur. Our framework helps to create clarity in these multiple relationships, thus assisting project managers in avoiding biased ML-outcomes.

Artificial Intelligence (AI) provides organizations with vast opportunities of deploying AI for competitive advantage such as improving processes, and creating new or enriched products and services. However, the failure rate of projects on implementing AI in organizations is still high, and prevents organizations from fully seizing the potential that AI exhibits. To contribute to closing this gap, we seize the unique opportunity to gain insights from five organizational cases. In particular, we empirically investigate how the unique characteristics of AI – i.e. experimental character, context sensitivity, black box character, and learning requirements – induce challenges into project management, and how these challenges are addressed in organizational (socio-technical) contexts. This shall provide researchers with an empirical and conceptual foundation for investigating the cause-effect relationships between the characteristics of AI, project management, and organizational change. Practitioners can benchmark their own practices against the insights to increase the success rates of future AI implementations.

New technologies, changing customer expectations and a competitive business environment impact financial firms and their workforces in Switzerland. Financial firms are aware of the need to transform to be successful in the future. The measures and best practices they apply are analyzed and presented in this study. The analysis showed that there is no single successful approach, and that successful firms engage in a variety of activities to transform their workforce, learn fast and are capable to adapt to changing needs. The analysis also showed that the focus of financial firms is to educate, to re-educate and to create a culture of change in the workforce. The objective of this study was to examine how financial firms transform in the light of the outlined market and technology developments. More precisely, what measures do financial firms take regarding their workforces to meet changing customer expectations, to adopt new technologies, and to stay competitive in the market. Customer experience is the key driver of change in the financial industry. Financial firms have recognized that customer needs are changing based on customer centric experiences from other industries. In the new era of financial industry, where switching costs are low the bargaining power of customers has significantly increased. Digital workforce enablement is inevitable in a world where every interaction becomes digital. Financial firms are taking measures to prepare their workforce to master becoming more digital and interact with the customers accordingly. They are offering educational content on digital technology and enable digital collaboration through digital collaboration tools. Incubation and innovation are key transformational aspects since the pace and dynamic of the financial industry has significantly changed. Financial firms need to incubate and innovate faster, adapt to customer needs and implement new technologies in a way that benefits the organization. Financial firms take measures to embrace an innovative mindset in the work-force, pursue novel partnerships in ecosystems and enable the so called “startup mentality” in their organizations. Agility and agile culture is needed to foster an innovation and incubation on an organizational level The study found out that financial firms are moving away from rigid hierarchical structures to a more agile way of working. Financial firms are aware that working in teams and generating bottom-up innovations have become a necessity. Also, new performance systems are introduced as measures to award team spirit and collaborative working. The simplification of the application landscape is inevitable for financial firms to increase speed-to-market. Over the years, financial firms embedded many applications in their existing IT landscape which led to slowness and inefficiencies. The interviewed leaders recognize that the IT legacy is an obstacle for speed-to-market and good customer experience. IT legacy also hinders financial firms to effectively compete with new players entering the market. New players can build their application landscape from scratch and in a customer-oriented way which allow new entrants to provide exceptional customer experience at affordable prices. Automation & efficiency programs are needed because financial firms face operational efficiency pressures due to competition and changing regulations. Financial firms are much aware that 40% to 60% of current jobs in the financial industry could be automated in the future. Thus, financial firms are preparing their workforces with education and re-education measures, create new job roles and promote self-directed learning. Particularly, the study found out that the responsibility to prepare for the future is shifting from employer to employees. The employer provides the possibilities and it is up to the employees to prepare for future job roles. The study covered the measures taken by the financial firms to transform. However, our study shows that not enough urgency in the workforce is created to achieve the desired changes. Only if the measures can drive change on an organizational level, a financial firm can go through a successful transformation and remain competitive in the future.

Artificial Intelligence (AI) is considered being a disruptive force for existing companies and a promising avenue towards competitive advantage. A myriad of companies started investing in AI initiatives. However, a significant number of AI projects is not successfully deployed. Taking a closer look at financial service organizations, we aim at contributing to closing the gap between understanding the potential of AI and proactively leveraging the latter. We draw on affordance theory and socio-technical systems (STS) theory to identify the required socio-technical changes to actualize affordances of AI in financial service organizations. We present preliminary findings from a multiple case study approach with five financial service organizations based on rigorous interview coding that yields first insights into AI affordances. Building up on this, we will prioritize and structure future in-depth case studies to investigate how to orchestrate AI-induced changes in STS for actualizing AI affordances.

Künstliche Intelligenz bietet Unternehmen neue Möglichkeiten Prozesse, Produkte, Dienstleistungen und Geschäftsmodelle zu innovieren und bestehende zu verändern. Daher wird das professionelle Management Künstlicher Intelligenz in Unternehmen zu einer zentralen Aufgabe, um die neuen Wertversprechen mit produktiven Systemen zu realisieren. Der Beitrag stellt das St. Galler Management Modell für KI (SGMM-KI) vor und zeigt sieben Handlungsfelder für den betrieblichen Einsatz von KI: (1) Management von Künstlicher Intelligenz, (2) Organisation des Betriebs, (3) Rechtliche Gestaltung, (4) Regulierung und Compliance, (5) Lebenszyklus-Management, (6) Management der Technologie-Infrastruktur, sowie (7) Cybersicherheit. Der vorliegende Artikel leitet konkrete erste Schritte an und richtet sich primär an Geschäftsleitungsmitglieder, IT- und Innovationsverantwortliche sowie Projektleiter, welche die neuen Wertversprechen der KI in der betrieblichen Praxis verwirklichen möchten.