Abstract Within the last decade, application of “artificial intelligence” and “machine learning” has become popular across multiple disciplines, especially in information systems. The two terms are still used inconsistently academia industry—sometimes as synonyms, sometimes with different meanings. With this work, we try to clarify relationship between these concepts. We review relevant literature develop a conceptual framework specify role machine learning building (artificial) intelligent...

AI advice is becoming increasingly popular, e.g., in investment and medical treatment decisions. As this typically imperfect, decision-makers have to exert discretion as whether actually follow that advice: they "appropriately" rely on correct turn down incorrect advice. However, current research appropriate reliance still lacks a common definition well an operational measurement concept. Additionally, no in-depth behavioral experiments been conducted help understand the factors influencing...

Abstract Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm that emphasizes the importance of enhancing data systematically and at scale to build effective efficient AI-based systems. The novel complements recent model-centric AI, which focuses on improving performance systems based changes in model using a fixed set data. objective this article is introduce practitioners researchers from field Business Information Systems Engineering (BISE) data-centric...

Abstract The field of artificial intelligence (AI) is advancing quickly, and systems can increasingly perform a multitude tasks that previously required human intelligence. Information facilitate collaboration between humans AI such their individual capabilities complement each other. However, there lack consolidated design guidelines for information facilitating the systems. This work examines how agent transparency affects trust task outcomes in context human-AI collaboration. Drawing on...

Recent work has proposed artificial intelligence (AI) models that can learn to decide whether make a prediction for an instance of task or delegate it human by considering both parties' capabilities. In simulations with synthetically generated context-independent predictions, delegation help improve the performance human-AI teams -- compared humans AI model completing alone. However, so far, remains unclear how perform and they perceive when are aware delegated instances them. experimental...

Business models have been a concept widely discussed over the last 20 years. The increasing availability of data and growing capability to exploit them with analytics has sparked new set discussions, though: it is claimed that bring bear entirely "data-based" or "data-driven" business models. However, there neither common understanding these nor ways existing are transformed into those. This paper aims create coherent framework infusion by analytics. Contrasting popular views, our conceptual...

The application of “machine learning” and “artificial intelligence” has become popular within the last decade. Both terms are frequently used in science media, sometimes interchangeably, with different meanings. In this work, we aim to clarify relationship between these and, particular, specify contribution machine learning artificial intelligence. We review relevant literature present a conceptual framework which clarifies role build (artificial) intelligent agents. Hence, seek provide more...

In the last decade, applying supervised machine learning (SML) has become increasingly popular in information systems (IS) field. However, SML results rely on many different data-preprocessing techniques, algorithms, and ways to implement them, which contributed an inconsistency way researchers have documented their efforts and, thus, degree others can reproduce results. one sense, we understand this given goals motivations for applications vary research area's rapid evolution. IS community,...

In AI-assisted decision-making, a central promise of having human-in-the-loop is that they should be able to complement the AI system by overriding its wrong recommendations. practice, however, we often see humans cannot assess correctness recommendations and, as result, adhere or override correct advice. Different ways relying on have immediate, yet distinct, implications for decision quality. Unfortunately, reliance and quality are inappropriately conflated in current literature...

In AI-assisted decision-making, a central promise of having human-in-the-loop is that they should be able to complement the AI system by overriding its wrong recommendations. practice, however, we often see humans cannot assess correctness recommendations and, as result, adhere or override correct advice. Different ways relying on have immediate, yet distinct, implications for decision quality. Unfortunately, reliance and quality are inappropriately conflated in current literature...

Consumers often resist adopting innovations, even in cases where they acknowledge that these would be beneficial for them. Cognitive biases consumers' decision-making may trigger resistance to innovation. We explore cognitive biases' effects innovation adoption decisions. Further, we investigate how digital nudging can used mitigate this order increase the likelihood an build a set of hypotheses and test them quasi-field experiment with 821 participants. first show occurrence correlates up...

The digital transformation offers new opportunities for organizations to expand their existing service portfolio in order achieve competitive advantages. A popular way create customer value is the offer of analytics-based services (ABS)-services that apply analytical methods data empower customers make better decisions and solve complex problems. However, research still lacks provide a profound conceptualization this novel type. Similarly, actionable insights on how purposefully establish...

Companies more and rely on predictive services which are constantly monitoring analyzing the available data streams for better service offerings. However, sudden or incremental changes in those a challenge validity proper functionality of over time. We develop framework allows to characterize differentiate with regard their ongoing validity. Furthermore, this work proposes research agenda worthwhile topics improve long-term services. In our work, we especially focus different scenarios true...

Many important decisions in daily life are made with the help of advisors, e.g., about medical treatments or financial investments. Whereas past, advice has often been received from human experts, friends, family, advisors based on artificial intelligence (AI) have become more and present nowadays. Typically, generated by AI is judged a either deemed reliable rejected. However, recent work shown that not always beneficial, as humans to be unable ignore incorrect advice, essentially...

Machine learning (ML) models are increasingly being used in application domains that often involve working together with human experts. In this context, it can be advantageous to defer certain instances a single expert when they difficult predict for the ML model. While previous work has focused on scenarios one distinct expert, many real-world situations several experts varying capabilities may available. work, we propose an approach trains classification model complement of multiple By...