Characteristics of Contemporary Artificial Intelligence Technologies and Implications for IS Research

Artificial Intelligence (AI) is often presented as a new phenomenon that is primarily driven by advances in contemporary machine learning technologies. Despite the steep rise, conceptualizations of contemporary AI technologies tend to be vague in many studies. This is problematic not only for positioning and focusing such research, but also for theorizing on the pervasive AI phenomenon. This paper presents a systematic literature review to understand and synthesize distinctive characteristics of contemporary AI technologies. In the course of our ongoing research, the preliminary findings encompass the changing role of data, feature extraction, adaptivity, transparency, and biases. With our future research, we seek to provide guidance on the conceptualizations of AI in IS research and to facilitate a more nuanced and focused theorization of AI in future IS studies

SENSEMAKING IN AI-BASED DIGITAL INNOVATIONS: INSIGHTS FROM A MANUFACTURING CASE STUDY

Organizations strive to innovate with Artificial Intelligence (AI) to tap new value potentials and outperform their competition. However, despite the enormous expectations associated with AI, incorporating the latter induces novel uncertainties and can even result in business value destruction. Therefore, organizations innovating with AI must manage these AI-induced uncertainties in their sensemaking process. Drawing on an exploratory case study, we investigate organizational sensemaking in two AI-based digital innovation projects at a globally leading automotive manufacturer. We account for the properties by which AI differs from traditional information systems and carve out how distinct AI properties unfold in AI-based digital innovations. We deduce four AI sensemaking mechanisms (i.e., cognition, interaction, regulation, and concretization) to understand better how AI challenges digital innovation endeavors in organizations

Effectiveness of Example-Based Explanations to Improve Human Decision Quality in Machine Learning Forecasting Systems

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\u27s 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

Explanation Interfaces for Sales Forecasting

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

Becoming Certain About the Uncertain: How AI Changes Proof-of-Concept Activities in Manufacturing – Insights from a Global Automotive Leader

In this paper, we examine Proof-of-Concept activities in the presence of Artificial Intelligence (AI). To this end, we conducted an exploratory, revelatory case study at a leading automotive OEM that constantly explores new technologies to improve its manufacturing processes. We highlight how AI properties affect specifics in project execution and how they are addressed within the focal company. We carved out four key areas affecting underlying activities, i.e., data assessment, process alignment, value orientation, and AI empowerment. With our findings, we provide practical insights into AI-related challenges and corresponding pathways for action. Drawn upon, we develop novel, timely, and actionable recommendations for AI project leaders planning to implement this novel technology in manufacturing. This shall provide empirically grounded and conceptually sound guidance for researchers and practitioners alike, and ultimately foster the success of AI in manufacturing