Advances in generative AI and the widespread proliferation of LLM-based applications have created a number of opportunities for designing effective and intelligent human-AI interfaces. Conversational User Interfaces (CUIs) have enabled humans to interact with machines more naturally across several domains and applications. People are increasingly familiar with conversational interactions mediated by technology due to the widespread use of mobile technologies, social networks, pervasive computing, and the rapid adoption of large language models that power conversational agents. Based on the recent advances in conversational AI, due to the proliferation of LLMs, there are clear signs that the future of human-computer interaction will have a significant conversational component. In the context of ever-lowering barriers to accessibility to technologies, digital applications, and generative AI, this tutorial will showcase the benefits of employing conversational interfaces for human-AI decision making, health and well-being, and crowd computing. We will discuss the potential of conversational interfaces in facilitating and mediating people’s interactions with AI systems and the opportunities and challenges that lie at this intersection from the broad standpoint of intelligent user interfaces. This third incarnation of this tutorial will include interactive elements and discussions, providing participants with practical insights to inform the design of effective conversational interfaces.

Explainable artificial intelligence (XAI) methods are being proposed to help interpret and understand how AI systems reach specific predictions. Inspired by prior work on conversational user interfaces, we argue that augmenting existing XAI methods with conversational user interfaces can increase user engagement and boost user understanding of the AI system. In this paper, we explored the impact of a conversational XAI interface on users’ understanding of the AI system, their trust, and reliance on the AI system. In comparison to an XAI dashboard, we found that the conversational XAI interface can bring about a better understanding of the AI system among users and higher user trust. However, users of both the XAI dashboard and conversational XAI interfaces showed clear over-reliance on the AI system. Enhanced conversations powered by large language model (LLM) agents amplified over-reliance. Based on our findings, we reason that the potential cause of such over-reliance is the illusion of explanatory depth that is concomitant with both XAI interfaces. Our findings have important implications for designing effective conversational XAI interfaces to facilitate appropriate reliance and improve human-AI collaboration.

Research on trust in AI is limited to several trustors (e.g., end-users) and trustees (especially AI systems), and empirical explorations remain in laboratory settings, overlooking factors that impact trust relations in the real world. Here, we broaden the scope of research by accounting for the supply chains that AI systems are part of. To this end, we present insights from an in-situ, empirical, study of LLM supply chains. We conducted interviews with 71 practitioners, and analyzed their (collaborative) practices using the lens of trust drawing from literature in organizational psychology. Our work reveals complex trust dynamics at the junctions of the chains, with interactions between diverse technical artifacts, individuals, or organizations. These junctions might constitute terrain for uncalibrated reliance when trustors lack supply chain knowledge or power dynamics are at play. Our findings bear implications for AI researchers and policymakers to promote AI governance that fosters calibrated trust.

Human intervention is claimed to safeguard decision-subjects’ rights in algorithmic decision-making and contribute to their fairness perceptions. However, how decision-subjects perceive hybrid decision-maker configurations (i.e., combining humans and algorithms) is unclear. We address this gap through a mixed-methods study in an algorithmic policy enforcement context. Through qualitative interviews (Study 1; N1 = 21), we identify three characteristics (i.e., decision-maker’s profile, model type, input data provenance) that affect how decision-subjects perceive decision-makers’ ability, benevolence, and integrity (ABI). Through a quantitative study (Study 2; N2 = 223), we then systematically evaluate the individual and combined effects of these characteristics on decision-subjects’ perceptions towards decision-makers, and fairness perceptions. We found that only decision-maker’s profile contributes to perceived ability, benevolence, and integrity. Interestingly, the effect of decision-maker’s profile on fairness perceptions was mediated by perceived ability and integrity. Our findings have design implications for ensuring effective human intervention as a protection against harmful algorithmic decisions.

Recent advances in generative AI have precipitated a proliferation of novel writing assistants. These systems typically rely on multilingual large language models (LLMs), providing globalized workers the ability to revise or create diverse forms of content in different languages. However, there is substantial evidence indicating that the performance of multilingual LLMs varies between languages. Users who employ writing assistance for multiple languages are therefore susceptible to disparate output quality. Importantly, recent research has shown that people tend to generalize algorithmic errors across independent tasks, violating the behavioral axiom of choice independence. In this paper, we analyze whether user utilization of novel writing assistants in a charity advertisement writing task is affected by the AI's performance in a second language. Furthermore, we quantify the extent to which these patterns translate into the persuasiveness of generated charity advertisements, as well as the role of peoples' beliefs about LLM utilization for their donation choices. Our results provide evidence that writers who engage with an LLM-based writing assistant violate choice independence, as prior exposure to a Spanish LLM reduces subsequent utilization of an English LLM. While these patterns do not affect the aggregate persuasiveness of the generated advertisements, people's beliefs about the source of an advertisement (human versus AI) do. In particular, Spanish-speaking female participants who believed that they read an AI-generated advertisement strongly adjusted their donation behavior downwards. Furthermore, people are generally not able to adequately differentiate between human-generated and LLM-generated ads. Our work has important implications on the design, development, integration, and adoption of multilingual LLMs as assistive agents - particularly in writing tasks.

Since the explosion in popularity of ChatGPT, large language models (LLMs) have continued to impact our everyday lives. Equipped with external tools that are designed for a specific purpose (e.g., for flight booking or an alarm clock), LLM agents exercise an increasing capability to assist humans in their daily work. Although LLM agents have shown a promising blueprint as daily assistants, there is a limited understanding of how they can provide daily assistance based on planning and sequential decision making capabilities. We draw inspiration from recent work that has highlighted the value of g'LLM-modulo' setups in conjunction with humans-in-the-loop for planning tasks. We conducted an empirical study (N = 248) of LLM agents as daily assistants in six commonly occurring tasks with different levels of risk typically associated with them (e.g., flight ticket booking and credit card payments). To ensure user agency and control over the LLM agent, we adopted LLM agents in a plan-then-execute manner, wherein the agents conducted step-wise planning and step-by-step execution in a simulation environment. We analyzed how user involvement at each stage affects their trust and collaborative team performance. Our findings demonstrate that LLM agents can be a double-edged sword - (1) they can work well when a high-quality plan and necessary user involvement in execution are available, and (2) users can easily mistrust the LLM agents with plans that seem plausible. We synthesized key insights for using LLM agents as daily assistants to calibrate user trust and achieve better overall task outcomes. Our work has important implications for the future design of daily assistants and human-AI collaboration with LLM agents.

The ability to make appropriate delegation decisions is an important prerequisite of effective human-AI collaboration. Recent work, however, has shown that people struggle to evaluate AI systems in the presence of forecasting errors, falling well short of relying on AI systems appropriately. We use a pre-registered crowdsourcing study (N = 611) to extend this literature by two underexplored crucial features of human AI decision-making: choice independence and error type. Subjects in our study repeatedly complete two prediction tasks and choose which predictions they want to delegate to an AI system. For one task, subjects receive a decision heuristic that allows them to make informed and relatively accurate predictions. The second task is substantially harder to solve, and subjects must come up with their own decision rule. We systematically vary the AI system's performance such that it either provides the best possible prediction for both tasks or only for one of the two. Our results demonstrate that people systematically violate choice independence by taking the AI's performance in an unrelated second task into account. Humans who delegate predictions to a superior AI in their own expertise domain significantly reduce appropriate reliance when the model makes systematic errors in a complementary expertise domain. In contrast, humans who delegate predictions to a superior AI in a complementary expertise domain significantly increase appropriate reliance when the model systematically errs in the human expertise domain. Furthermore, we show that humans differentiate between error types and that this effect is conditional on the considered expertise domain. This is the first empirical exploration of choice independence and error types in the context of human-AI collaboration. Our results have broad and important implications for the future design, deployment, and appropriate application of AI systems.

Powerful predictive AI systems have demonstrated great potential in augmenting human decision making. Recent empirical work has argued that the vision for optimal human-AI collaboration requires ‘appropriate reliance’ on AI systems. However, accurately estimating the trustworthiness of AI advice at the instance level is quite challenging, especially in the absence of performance feedback pertaining to the AI system. In practice, the performance disparity of machine learning models on out-of-distribution data makes the dataset-specific performance feedback unreliable in human-AI collaboration. Inspired by existing literature on critical thinking and mindsets, we propose debugging an AI system as an intervention to foster appropriate reliance. This paper explores whether a critical evaluation of AI performance within a debugging setting can better calibrate users’ assessment of an AI system. Through a quantitative empirical study (N = 234), we found that our proposed debugging intervention does not work as expected in facilitating appropriate reliance. Instead, we observe a decrease in reliance on the AI system — potentially resulting from an early exposure to the AI system’s weakness. Our findings have important implications for designing effective interventions to facilitate appropriate reliance and better human-AI collaboration.

When using web search engines to conduct inquiries on debated topics, searchers' interactions with search results are commonly affected by a combination of searcher and system biases. While prior work has mainly investigated these biases in isolation, there is a lack of a comprehensive understanding of web search on debated topics. Addressing this gap, we conducted an exploratory user study (N = 255), aimed at advancing the understanding of the intricate searcher-system interplay. Particularly, we investigated the relations between (i) search system exposure, searchers' attitude strength, prior knowledge, and receptiveness to opposing views, (ii) search interactions, and (iii) post-search epistemic states. We observed that search interaction was shaped by search system exposure, attitude strength, and prior knowledge, and that attitude change was influenced by the level of confirmation bias and initial attitude strength, but not search system exposure. Insights from this work suggest the need to adapt interventions that mitigate the risks of searcher and system bias to searchers' nuanced pre-search epistemic states. They further emphasize the threat of customizing the search ranking to enhance user satisfaction in the context of debated topics to responsible opinion formation.

Conversational User Interfaces (CUIs) have been argued to have advantages over traditional GUIs due to having a more human-like interaction. The growing popularity of conversational agents has enabled humans to interact with machines more naturally. People are increasingly familiar with conversational interactions mediated by technology due to the widespread use of mobile devices and messaging services and a hungry market for conversational agents. Based on the recent advances in conversational AI, due to the proliferation of large language models, there are clear signs that the future of human-computer interaction will have a significant conversational component. Today, over two-thirds of the population on our planet has access to the Internet, with ever-lowering barriers to accessibility. This tutorial will showcase the benefits of employing novel conversational interfaces for crowd computing, human-AI decision making, health and well-being, and information retrieval. Given the widespread adoption of AI systems across several domains, we will discuss the potential of conversational interfaces in facilitating and mediating people's interactions with AI systems and the opportunities and challenges that lie at this intersection from the user modeling and personalization standpoint. The tutorial will include interactive elements and discussions and provide participants with practical insights to inform the design of effective conversational interfaces.

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.

Pilot studies are an essential cornerstone of the design of crowdsourcing campaigns, yet they are often only mentioned in passing in the scholarly literature. A lack of details surrounding pilot studies in crowdsourcing research hinders the replication of studies and the reproduction of findings, stalling potential scientific advances. We conducted a systematic literature review on the current state of pilot study reporting at the intersection of crowdsourcing and HCI research. Our review of ten years of literature included 171 articles published in the proceedings of the Conference on Human Computation and Crowdsourcing (AAAI HCOMP) and the ACM Digital Library. We found that pilot studies in crowdsourcing research (i.e., crowd pilot studies) are often under-reported in the literature. Important details, such as the number of workers and rewards to workers, are often not reported. On the basis of our findings, we reflect on the current state of practice and formulate a set of best practice guidelines for reporting crowd pilot studies in crowdsourcing research. We also provide implications for the design of crowdsourcing platforms and make practical suggestions for supporting crowd pilot study reporting.

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.

Existing research in measuring and mitigating gender bias predominantly centers on English, overlooking the intricate challenges posed by non-English languages and the Global South. This paper presents the first comprehensive study delving into the nuanced landscape of gender bias in Hindi, the third most spoken language globally. Our study employs diverse mining techniques, computational models, field studies and sheds light on the limitations of current methodologies. Given the challenges faced with mining gender biased statements in Hindi using existing methods, we conducted field studies to bootstrap the collection of such sentences. Through field studies involving rural and low-income community women, we uncover diverse perceptions of gender bias, underscoring the necessity for context-specific approaches. This paper advocates for a community-centric research design, amplifying voices often marginalized in previous studies. Our findings not only contribute to the understanding of gender bias in Hindi but also establish a foundation for further exploration of Indic languages. By exploring the intricacies of this understudied context, we call for thoughtful engagement with gender bias, promoting inclusivity and equity in linguistic and cultural contexts beyond the Global North.

Advances in generative AI, the proliferation of large multimodal models (LMMs), and democratized open access to these technologies have direct implications for the production and diffusion of misinformation. In this prequel, we address tackling misinformation in the unique and increasingly popular context of podcasts. The rise of podcasts as a popular medium for disseminating information across diverse topics necessitates a proactive strategy to combat the spread of misinformation. Inspired by the proven effectiveness of auditory alerts in contexts like collision alerts for drivers and error pings in mobile phones, our work envisions the application of auditory alerts as an effective tool to tackle misinformation in podcasts. We propose the integration of suitable auditory alerts to notify listeners of potential misinformation within the podcasts they are listening to, in real-time and without hampering listening experiences. We identify several opportunities and challenges in this path and aim to provoke novel conversations around instruments, methods, and measures to tackle misinformation in podcasts.

Web search has evolved into a platform people rely on for opinion formation on debated topics. Yet, pursuing this search intent can carry serious consequences for individuals and society and involves a high risk of biases. We argue that web search can and should empower users to form opinions responsibly and that the information retrieval community is uniquely positioned to lead interdisciplinary efforts to this end. Building on digital humanism---a perspective focused on shaping technology to align with human values and needs---and through an extensive interdisciplinary literature review, we identify challenges and research opportunities that focus on the searcher, search engine, and their complex interplay. We outline a research agenda that provides a foundation for research efforts toward addressing these challenges.

Human intelligence continues to be essential in building ground-truth data, training sets, and for evaluating a plethora of systems. The democratized and distributed nature of online crowd work - an attractive and accessible feature that has led to the proliferation of the paradigm - has also meant that crowd workers may not always feel connected to their remote peers. Despite the prevalence of collaborative crowdsourcing practices, workers on many microtask crowdsourcing platforms work on tasks individually and are seldom directly exposed to other crowd workers. In this context, improving worker engagement on microtask crowdsourcing platforms is an unsolved challenge. At the same time, fostering a sense of community among workers can improve the sustainability and working conditions in crowd work. This work aims to increase worker engagement in conversational microtask crowdsourcing by leveraging evolving avatars that workers can customize as they progress through monotonous task batches. We also aim to improve group identifcation in individual tasks by creating a community space where workers can share their avatars and feelings on task completion. To this end, we carried out a preregistered between-subjects controlled study (N = 680) spanning fve experimental conditions and two task types. We found that evolving and customizable worker avatars can increase worker retention. The prospect of sharing worker avatars and task-related feelings in a community space did not consistently afect group identifcation. Our exploratory analysis indicated that workers who identify themselves as crowd workers experienced greater intrinsic motivation, subjective engagement, and perceived workload. Furthermore, we discuss how task diferences shape the relative efectiveness of our interventions. Our fndings have important theoretical and practical implications for designing conversational crowdsourcing tasks and in shaping new directions for research to improve crowd worker experiences.

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.

Concepts are an important construct in semantics, based on which humans understand the world with various levels of abstraction. With the recent advances in explainable artificial intelligence (XAI), concept-level explanations are receiving an increasing amount of attention from the broad research community. However, laypeople may find such explanations difficult to digest due to the potential knowledge gap and the concomitant cognitive load. Inspired by prior work that has explored analogies and sensemaking, we argue that augmenting concept-level explanations with analogical inference information from commonsense knowledge can be a potential solution to tackle this issue. To investigate the validity of our proposition, we first designed an effective analogy-based explanation generation method and collected 600 analogy-based explanations from 100 crowd workers. Next, we proposed a set of structured dimensions for the qualitative assessment of such explanations, and conducted an empirical evaluation of the generated analogies with experts. Our findings revealed significant positive correlations between the qualitative dimensions of analogies and the perceived helpfulness of analogy-based explanations, suggesting the effectiveness of the dimensions. To understand the practical utility and the effectiveness of analogybased explanations in assisting human decision-making, we conducted a follow-up empirical study (N = 280) on a skin cancer detection task with non-expert humans and an imperfect AI system. Thus, we designed a between-subjects study spanning five different experimental conditions with varying types of explanations. The results of our study confirmed that a knowledge gap can prevent participants from understanding concept-level explanations. Consequently, when only the target domain of our designed analogy-based explanation was provided (in a specific experimental condition), participants demonstrated relatively more appropriate reliance on the AI system. In contrast to our expectations, we found that analogies were not effective in fostering appropriate reliance. We carried out a qualitative analysis of the open-ended responses from participants in the study regarding their perceived usefulness of explanations and analogies. Our findings suggest that human intuition and the perceived plausibility of analogies may have played a role in affecting user reliance on the AI system. We also found that the understanding of commonsense explanations varied with the varying experience of the recipient user, which points out the need for further work on personalization when leveraging commonsense explanations. In summary, although we did not find quantitative support for our hypotheses around the benefits of using analogies, we found considerable qualitative evidence suggesting the potential of high-quality analogies in aiding non-expert users in their decision making with AI-assistance. These insights can inform the design of future methods for the generation and use of effective analogy-based explanations.