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...

Research in artificial intelligence (AI)-assisted decision-making is experiencing tremendous growth with a constantly rising number of studies evaluating the effect AI and without techniques from field explainable (XAI) on human performance. However, as tasks experimental setups vary due to different objectives, some report improved user performance through XAI, while others only negligible effects. Therefore, this article, we present an initial synthesis existing research XAI using...

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...

In recent years, the rapid development of AI systems has brought about benefits intelligent services but also concerns security and reliability. By fostering appropriate user reliance on an system, both complementary team performance reduced human workload can be achieved. Previous empirical studies have extensively analyzed impact factors ranging from task, behavior trust in context one-step decision making. However, tasks with complex semantics that require multi-step workflows remains...

Abstract As organizations accumulate vast amounts of data for analysis, a significant challenge remains in fully understanding these datasets to extract accurate information and generate real-world impact. Particularly, the high dimensionality lack sufficient documentation, specifically provision metadata, often limit potential exploit full value via analytical methods. To address issues, this study proposes hybrid approach metadata generation, that leverages both in-depth knowledge domain...

While recent advances in AI-based automated decision-making have shown many benefits for businesses and society, they also come at a cost.It has long been known that high level of automation decisions can lead to various drawbacks, such as bias deskilling.In particular, the deskilling knowledge workers is major issue, are same people who should train, challenge evolve AI.To address this we conceptualize new class DSS, namely Intelligent Decision Assistance (IDA) based on literature review...

We present a simplified design of an ear-centered sensing system built around the OpenBCI Cyton & Daisy biosignal amplifiers and flex-printed cEEGrid ear-EEG electrodes. This reduces number components that need to be sourced, mechanical artefacts on recording data through better cable placement, simplifies assembly. Besides describing how replicate use system, we highlight promising application scenarios, particularly observation large-amplitude activity patterns (e.g., facial muscle...

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

Many decision processes are based on image analysis, for instance, medical diagnoses or visual monitoring of industrial processes. At the same time, advances in deep learning have significantly improved information extraction from images. While recent research strongly focuses extracting single images, potential mining entire collections has been neglected so far. In this work, we develop design knowledge to use decision-making. We derive requirements image-mining-based support systems...

While recent advances in AI-based automated decision-making have shown many benefits for businesses and society, they also come at a cost. It has long been known that high level of automation decisions can lead to various drawbacks, such as bias deskilling. In particular, the deskilling knowledge workers is major issue, are same people who should train, challenge evolve AI. To address this we conceptualize new class DSS, namely Intelligent Decision Assistance (IDA) based on literature review...