People’s online information processing is strongly shaped by social influence, and large language models (LLMs) now enable social bots to manipulate such influence at scale. This paper examines the effects of LLM-driven adversarial social influence—a strategy in which automated agents employ LLMs to distort truth by making misinformation appear credible or by undermining factual news—on how people evaluate and share information. Across two pre-registered, randomized experiments, we first show that exposure to LLM-driven adversarial social influence significantly reduces people’s ability to judge the veracity of news and lowers their discernment between sharing true versus false content. We then test two credibility prompts: AI-generated content detectors and warnings, as potential interventions. Results show that both prompts mitigate some harms such as by improving misinformation detection, though their effectiveness were dependent on the context. We conclude by discussing the risks of LLM-driven adversarial social bots and the implications for designing interventions to combat misinformation.

Crew-reducing exhibits promise for various benefits in the aviation industry. However, there is limited understanding regarding public acceptance. Using an experimental design deployed with a vignette-based online study, we investigated individuals’ negative emotion, trust, risk acceptance, and willingness to ride toward Single Pilot Operations (SPO) and Dual Pilot Operations (DPO). Results established that people preferred DPO flights, relying on affect heuristic. Specifically, people’s negative emotion associated with SPO decreases their trust, which subsequently results in lower levels of willingness to ride and risk acceptance. Furthermore, we observed that people are less likely to accept risk evoked by intelligent autonomous system in DPO flights, which likely due to their psychological model about two pilots in the cockpit. Findings from this research highlight the importance of users’ initially positive affects about intelligent equipment in the cockpit. Other theoretical and practical implications for narrowing the acceptable gap between SPO and DPO are discussed.

With the rapid development of AI-based decision aids, different forms of AI assistance have been increasingly integrated into the human decision making processes. To best support humans in decision making, it is essential to quantitatively understand how diverse forms of AI assistance influence humans' decision making behavior. To this end, much of the current research focuses on the end-to-end prediction of human behavior using ``black-box'' models, often lacking interpretations of the nuanced ways in which AI assistance impacts the human decision making process. Meanwhile, methods that prioritize the interpretability of human behavior predictions are often tailored for one specific form of AI assistance, making adaptations to other forms of assistance difficult. In this paper, we propose a computational framework that can provide an interpretable characterization of the influence of different forms of AI assistance on decision makers in AI-assisted decision making. By conceptualizing AI assistance as the ``nudge'' in human decision making processes, our approach centers around modelling how different forms of AI assistance modify humans' strategy in weighing different information in making their decisions. Evaluations on behavior data collected from real human decision makers show that the proposed framework outperforms various baselines in accurately predicting human behavior in AI-assisted decision making. Based on the proposed framework, we further provide insights into how individuals with different cognitive styles are nudged by AI assistance differently.

Group decision making plays a crucial role in our complex and interconnected world. The rise of AI technologies has the potential to provide data-driven insights to facilitate group decision making, although it is found that groups do not always utilize AI assistance appropriately. In this paper, we aim to examine whether and how the introduction of a devil’s advocate in the AI-assisted group decision making processes could help groups better utilize AI assistance and change the perceptions of group processes during decision making. Inspired by the exceptional conversational capabilities exhibited by modern large language models (LLMs), we design four different styles of devil’s advocate powered by LLMs, varying their interactivity (i.e., interactive vs. non-interactive) and their target of objection (i.e., challenge the AI recommendation or the majority opinion within the group). Through a randomized human-subject experiment, we find evidence suggesting that LLM-powered devil’s advocates that argue against the AI model’s decision recommendation have the potential to promote groups’ appropriate reliance on AI. Meanwhile, the introduction of LLM-powered devil’s advocate usually does not lead to substantial increases in people’s perceived workload for completing the group decision making tasks, while interactive LLM-powered devil’s advocates are perceived as more collaborating and of higher quality. We conclude by discussing the practical implications of our findings.

AI assistance in decision-making has become popular, yet people's inappropriate reliance on AI often leads to unsatisfactory human-AI collaboration performance. In this paper, through three pre-registered, randomized human subject experiments, we explore whether and how the provision of second opinions may affect decision-makers' behavior and performance in AI-assisted decision-making. We find that if both the AI model's decision recommendation and a second opinion are always presented together, decision-makers reduce their over-reliance on AI while increase their under-reliance on AI, regardless whether the second opinion is generated by a peer or another AI model. However, if decision-makers have the control to decide when to solicit a peer's second opinion, we find that their active solicitations of second opinions have the potential to mitigate over-reliance on AI without inducing increased under-reliance in some cases. We conclude by discussing the implications of our findings for promoting effective human-AI collaborations in decision-making.

The increased integration of artificial intelligence (AI) technologies in human workflows has resulted in a new paradigm of AI-assisted decision making, in which an AI model provides decision recommendations while humans make the final decisions. To best support humans in decision making, it is critical to obtain a quantitative understanding of how humans interact with and rely on AI. Previous studies often model humans' reliance on AI as an analytical process, i.e., reliance decisions are made based on cost-benefit analysis. However, theoretical models in psychology suggest that the reliance decisions can often be driven by emotions like humans' trust in AI models. In this paper, we propose a hidden Markov model to capture the affective process underlying the human-AI interaction in AI-assisted decision making, by characterizing how decision makers adjust their trust in AI over time and make reliance decisions based on their trust. Evaluations on real human behavior data collected from human-subject experiments show that the proposed model outperforms various baselines in accurately predicting humans' reliance behavior in AI-assisted decision making. Based on the proposed model, we further provide insights into how humans' trust and reliance dynamics in AI-assisted decision making is influenced by contextual factors like decision stakes and their interaction experiences.

With the prevalence of AI assistance in decision making, a more relevant question to ask than the classical question of “are two heads better than one?’’ is how groups’ behavior and performance in AI-assisted decision making compare with those of individuals’. In this paper, we conduct a case study to compare groups and individuals in human-AI collaborative recidivism risk assessment along six aspects, including decision accuracy and confidence, appropriateness of reliance on AI, understanding of AI, decision-making fairness, and willingness to take accountability. Our results highlight that compared to individuals, groups rely on AI models more regardless of their correctness, but they are more confident when they overturn incorrect AI recommendations. We also find that groups make fairer decisions than individuals according to the accuracy equality criterion, and groups are willing to give AI more credit when they make correct decisions. We conclude by discussing the implications of our work.

The collection and curation of high-quality training data is crucial for developing text classification models with superior performance, but it is often associated with significant costs and time investment.Researchers have recently explored using large language models (LLMs) to generate synthetic datasets as an alternative approach.However, the effectiveness of the LLM-generated synthetic data in supporting model training is inconsistent across different classification tasks.To better understand factors that moderate the effectiveness of the LLMgenerated synthetic data, in this study, we look into how the performance of models trained on these synthetic data may vary with the subjectivity of classification.Our results indicate that subjectivity, at both the task level and instance level, is negatively associated with the performance of the model trained on synthetic data.We conclude by discussing the implications of our work on the potential and limitations of leveraging LLM for synthetic data generation 1 .

With the increased integration of AI technologies in human decision making processes, adversarial attacks on AI models become a greater concern than ever before as they may significantly hurt humans’ trust in AI models and decrease the effectiveness of human-AI collaboration. While many adversarial attack methods have been proposed to decrease the performance of an AI model, limited attention has been paid on understanding how these attacks will impact the human decision makers interacting with the model, and accordingly, how to strategically deploy adversarial attacks to maximize the reduction of human trust and reliance. In this paper, through a human-subject experiment, we first show that in AI-assisted decision making, the timing of the attacks largely influences how much humans decrease their trust in and reliance on AI—the decrease is particularly salient when attacks occur on decision making tasks that humans are highly confident themselves. Based on these insights, we next propose an algorithmic framework to infer the human decision maker’s hidden trust in the AI model and dynamically decide when the attacker should launch an attack to the model. Our evaluations show that following the proposed approach, attackers deploy more efficient attacks and achieve higher utility than adopting other baseline strategies.

With the development of mobile networks and intelligent equipment, as a new intelligent data sensing paradigm in large-scale sensor applications such as the industrial Internet of Things, mobile crowd sensing (MCS) assigns industrial sensing tasks to workers for data collection and sharing, which has created a bright future for building a strong industrial system and improving industrial services. How to design an effective worker selection mechanism to maximize the utility of crowdsourcing is the research hotspot of mobile sensing technologies. This article studies the problem of least workers selection to make large MCS system perform sensing tasks more effective and achieve certain coverage with certain constraints being meeting. A many-objective worker selection method is proposed to achieve the desired tradeoff and an optimization mechanism is designed based on the enhanced differential evolution algorithm to ensure data integrity and search solution optimality. The effectiveness of the proposed method is verified through a large scale of experimental evaluation datasets collected from real world.

Biased language is prevalent in today's online social media. To reduce the amount of online biased language, one critical first step is to accurately detect such biased language, ideally automatically. This is a challenging problem, however, as the annotated data necessary for training a biased language classifier is either scarce and costly (e.g., when collected from experts), or noisy and potentially biased on their own (e.g., when collected from crowd workers). The biased language classifier built based on these annotations may thus be inaccurate, and sometimes unfair (e.g., have systematic accuracy disparities across texts with different political leanings). In this paper, we propose a novel method, CLEARE, for biased language detection, in which we utilize self-supervised contrastive learning to enhance the biased language classifier---we learn a robust encoder of the textual data through solving a min-max optimization problem, so that the encoder could help achieve the best classification performance even if the worst data augmentation strategy is selected. Extensive evaluations suggest that CLEARE shows substantial improvements compared to the state-of-art biased language detection methods on several benchmark datasets, in terms of improving both the accuracy and the fairness of the detection.

Internet users make numerous decisions online on a daily basis. With the rapid advances in AI recently, AI-assisted decision making—in which an AI model provides decision recommendations and confidence, while the humans make the final decisions—has emerged as a new paradigm of human-AI collaboration. In this paper, we aim at obtaining a quantitative understanding of whether and when would human decision makers adopt the AI model’s recommendations. We define a space of human behavior models by decomposing the human decision maker’s cognitive process in each decision-making task into two components: the utility component (i.e., evaluate the utility of different actions) and the selection component (i.e., select an action to take), and we perform a systematic search in the model space to identify the model that fits real-world human behavior data the best. Our results highlight that in AI-assisted decision making, human decision makers’ utility evaluation and action selection are influenced by their own judgement and confidence on the decision-making task. Further, human decision makers exhibit a tendency to distort the decision confidence in utility evaluations. Finally, we also analyze the differences in humans’ adoption behavior of AI recommendations as the stakes of the decisions vary.

Misinformation on social media has become a serious concern. Marking news stories with credibility indicators, possibly generated by an AI model, is one way to help people combat misinformation. In this paper, we report the results of two randomized experiments that aim to understand the effects of AI-based credibility indicators on people's perceptions of and engagement with the news, when people are under social influence such that their judgement of the news is influenced by other people. We find that the presence of AI-based credibility indicators nudges people into aligning their belief in the veracity of news with the AI model's prediction regardless of its correctness, thereby changing people's accuracy in detecting misinformation. However, AI-based credibility indicators show limited impacts on influencing people's engagement with either real news or fake news when social influence exists. Finally, it is shown that when social influence is present, the effects of AI-based credibility indicators on the detection and spread of misinformation are larger as compared to when social influence is absent, when these indicators are provided to people before they form their own judgements about the news. We conclude by providing implications for better utilizing AI to fight misinformation.

This paper addresses an under-explored problem of AI-assisted decision-making: when objective performance information of the machine learning model underlying a decision aid is absent or scarce, how do people decide their reliance on the model? Through three randomized experiments, we explore the heuristics people may use to adjust their reliance on machine learning models when performance feedback is limited. We find that the level of agreement between people and a model on decision-making tasks that people have high confidence in significantly affects reliance on the model if people receive no information about the model’s performance, but this impact will change after aggregate-level model performance information becomes available. Furthermore, the influence of high confidence human-model agreement on people’s reliance on a model is moderated by people’s confidence in cases where they disagree with the model. We discuss potential risks of these heuristics, and provide design implications on promoting appropriate reliance on AI.