Empirical studies have extensively investigated human decision-making processes in various domains where AI systems are incorporated. However, comparing and replicating these studies can be challenging due to different experimental configurations. Moreover, the existing contexts often have limited scope and may not fully capture the complexity of real-world decision-making scenarios that are riddled with varying levels of uncertainty. Our framework addresses these practical gaps by providing a configurable and reproducible environment for conducting human-AI decision-making studies in the route planning domain that captures many complexities of real-world scenarios. Researchers can customize parameters, conditions, and factors involved in decision-making tasks to help address research and empirical gaps through rigorous experiments. With various modules such as map generation, chat components, and different AI systems available within the "DecisionTime"framework, researchers can effortlessly design experiments exploring multiple aspects of human-AI interaction and decision-making.

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While existing literature has explored and revealed several insights pertaining to the role of human factors (e.g., prior experience, domain knowledge) and attributes of AI systems (e.g., accuracy, trustworthiness), there is a limited understanding around how the important task characteristics of complexity and uncertainty shape human decision-making and human-AI team performance. In this work, we aim to address this research and empirical gap by systematically exploring how task complexity and uncertainty infuence human-AI decision-making. Task complexity refers to the load of information associated with a task, while task uncertainty refers to the level of unpredictability associated with the outcome of a task. We conducted a between-subjects user study (N = 258) in the context of a trip-planning task to investigate the impact of task complexity and uncertainty on human trust and reliance on AI systems. Our results revealed that task complexity and uncertainty have a signifcant impact on user reliance on AI systems. When presented with complex and uncertain tasks, users tended to rely more on AI systems while demonstrating lower levels of appropriate reliance compared to tasks that were less complex and uncertain. In contrast, we found that user trust in the AI systems was not infuenced by task complexity and uncertainty. Our fndings can help inform the future design of empirical studies exploring human-AI decision-making. Insights from this work can inform the design of AI systems and interventions that are better aligned with the challenges posed by complex and uncertain tasks. Finally, the lens of diagnostic versus prognostic tasks can inspire the operationalization of uncertainty in human-AI decision-making studies.

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With the integration of AI systems into our daily lives, human-AI collaboration has become increasingly prevalent. Prior work in this realm has primarily explored the effectiveness and performance of individual human and AI systems in collaborative tasks. While much of decision-making occurs within human peers and groups in the real world, there is a limited understanding of how they collaborate with AI systems. One of the key predictors of human-AI collaboration is the characteristics of the task at hand. Understanding the influence of task characteristics on human-AI collaboration is crucial for enhancing team performance and developing effective strategies for collaboration. Addressing a research and empirical gap, we seek to explore how the features of a task impact decision-making within human-AI group settings. In a 2 × 2 between-subjects study (N = 256) we examine the effects of task complexity and uncertainty on group performance and behaviour. The participants were grouped into pairs and assigned to one of four experimental conditions characterized by varying degrees of complexity and uncertainty. We found that high task complexity and high task uncertainty can negatively impact the performance of human-AI groups, leading to decreased group accuracy and increased disagreement with the AI system. We found that higher task complexity led to a higher efficiency in decision-making, while a higher task uncertainty had a negative impact on efficiency. Our findings highlight the importance of considering task characteristics when designing human-AI collaborative systems, as well as the future design of empirical studies exploring human-AI collaboration.@en

Recent advances in the performance of machine learning algorithms have led to the adoption of AI models in decision making contexts across various domains such as healthcare, finance, and education.Different research communities have attempted to optimize and evaluate human-AI team performance through empirical studies by increasing transparency of AI systems, or providing explanations to aid human understanding of such systems.However, the variety in decision making tasks considered and their operationalization in prior empirical work, has led to an opacity around how findings from one task or domain carry forward to another.The lack of a standardized means of considering task attributes prevents straightforward comparisons across decision tasks, thereby limiting the generalizability of findings.We argue that the lens of ‘task complexity’ can be used to tackle this problem of under-specification and facilitate comparison across empirical research in this area.To retrospectively explore how different HCI communities have considered the influence of task complexity in designing experiments in the realm of human-AI decision making, we survey literature and provide an overview of empirical studies on this topic.We found a serious dearth in the consideration of task complexity across various studies in this realm of research.Inspired by Robert Wood’s seminal work on the construct, we operationalized task complexity with respect to three dimensions (component, coordinative, and dynamic) and quantified the complexity of decision tasks in existing work accordingly.We then summarized current trends and proposed research directions for the future.Our study highlights the need to account for task complexity as an important design choice.This is a first step to help the scientific community in drawing meaningful comparisons across empirical studies in human-AI decision making and to provide opportunities to generalize findings across diverse domains and experimental settings.

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Natural Language Interfaces to Databases (NLIDB), also known as Text-to-SQL models, enable users with different levels of knowledge in Structured Query Language (SQL) to access relational databases without any programming effort. By translating natural languages into SQL query, not only do NLIDBs minimize the burden of memorizing the schema of databases and writing complex SQL queries, but they also allow non-experts to acquire information from databases in natural languages. However, existing NLIDBs largely fail to translate natural languages to SQL when they are complex, preventing them from being deployed in real-world scenarios and generalizing across unseen complex databases. In this paper, we explored the feasibility of decomposing complex user questions into multiple sub-questions - each with a reduced complexity - as a means to circumvent the problem of complex SQL generation. We investigated the feasibility of decomposing complex user questions in a manner that each sub-question is simple enough for existing NLIDBs to generate correct SQL queries, using non-expert crowd workers in juxtaposition with SQL experts. Through an empirical study on an NLIDB benchmark dataset, we found that crowd-powered decomposition of complex user questions led to an accuracy boost of an existing Text-to-SQL pipeline from 30% to 59% (96% accuracy boost). Similarly, decomposition by SQL experts resulted in boosting the accuracy to 76% (153% accuracy boost). Our findings suggest that crowd-powered decomposition can be a scalable alternative to producing the training data necessary to build machine learning models that can automatically decompose complex user questions, thereby improving Text-to-SQL pipelines.

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Entity cards are a common occurrence in today's web Search Engine Results Pages (SERPs). SERPs provide information on a complex information object in a structured manner. Typically, they combine data from several search verticals. They have been shown to: (i) increase users' engagement with the SERP; and (ii) improve decision making for certain types of searches (such as health searches). In this paper, we investigate whether the benefits of showing entity cards also extend to the Search as Learning (SAL) domain. Do learners learn more when entity cards are present on the SERP? To answer this question, we designed a series of learning-oriented search tasks (with a minimum search time of 15 minutes), and conducted a crowdsourced Interactive Information Retrieval (IIR) user study (N=144) with four interface conditions: (i) a control with no entity cards; (ii) displaying relevant entity cards; (iii) displaying somewhat relevant entity cards; and (iv) displaying non-relevant entity cards. Our results show that (i) entity cards do not have an effect on participants' learning, but (ii) they do significantly impact participants' search behaviours across a range of dimensions (such as the dwell time and search session duration).@en