Human Decision Research Articles

Beyond risk preferences in sequential decision-making: How probability representation, sequential structure and choice perseverance bias optimal search

Sequential decision-making, where choices are made one after the other, is an important aspect of our daily lives. For example, when searching for a job, an apartment, or deciding when to buy or sell a stock, people often have to make decisions without knowing what future opportunities might arise. These situations, which are known as optimal stopping problems, involve a risk associated with the decision to either stop or continue searching. However, previous research has not consistently found a clear connection between individuals’ search behavior in these tasks and their risk preferences as measured in controlled experimental settings. In this paper, we explore how particular characteristics of optimal stopping tasks affect people’s choices, extending beyond their stable risk preferences. We find that (1) the way the underlying sampling distribution is presented (whether it is based on experience or description), (2) the sequential presentation of options, and (3) the unequal frequencies of choices to reject versus to accept significantly bias people choices. These results shed light on the complex nature of decisions that unfold sequentially and emphasize the importance of incorporating context factors when studying human decision behavior.

The Algorithm and the Org Chart: How Algorithms Can Conflict with Organizational Structures

Algorithms are introducing changes to individuals? jobs, but do algorithms also lead to changes in the structures of organizations themselves? Organizational structures, as often formalized into organization (org) charts, are meant to facilitate coordinated decision-making. Yet our 10-month ethnographic study of a large online retail company reveals why the organizational structures that facilitate effective decision-making by humans may be in tension with the organizational structures that facilitate effective decision-making using algorithms. Our findings show that the human decision-makers needed small, divided-up sets of decisions, and they had previously accomplished this through how they structured individuals' roles and teams in the org chart. In contrast, when data scientists developed a new algorithm and first deployed it within organizational structures meant to support human decision-making, they realized that these small divided-up decision spaces were arbitrarily constraining the algorithm's search space. When not constrained in this manner, the algorithm could identify and recommend better solutions, but those optimal solutions did not always align with the structure of roles and teams in the org chart. This study suggests that as algorithms are integrated into the workplace, organization designs may begin to more explicitly reflect the contours of those algorithms' behaviors.

(De)Noise: Moderating the Inconsistency Between Human Decision-Makers

Prior research in psychology has found that people's decisions are often inconsistent. An individual's decisions vary across time, and decisions vary even more across people. Inconsistencies have been identified not only in subjective matters, like matters of taste, but also in settings one might expect to be more objective, such as sentencing, job performance evaluations, or real estate appraisals. In our study, we explore whether algorithmic decision aids can be used to moderate the degree of inconsistency in human decision-making in the context of real estate appraisal. In a large-scale human-subject experiment, we study how different forms of algorithmic assistance influence the way that people review and update their estimates of real estate prices. We find that both (i) asking respondents to review their estimates in a series of algorithmically chosen pairwise comparisons and (ii) providing respondents with traditional machine advice are effective strategies for influencing human responses. Compared to simply reviewing initial estimates one by one, the aforementioned strategies lead to (i) a higher propensity to update initial estimates, (ii) a higher accuracy of post-review estimates, and (iii) a higher degree of consistency between the post-review estimates of different respondents. While these effects are more pronounced with traditional machine advice, the approach of reviewing algorithmically chosen pairs can be implemented in a wider range of settings, since it does not require access to ground truth data.

The Explanation That Hits Home: The Characteristics of Verbal Explanations That Affect Human Perception in Subjective Decision-Making

Human-AI collaborative decision-making can achieve better outcomes than either party individually. The success of this collaboration can depend on whether the human decision-maker perceives the AI contribution as beneficial to the decision-making process. Beneficial AI explanations are often described as relevant, convincing, and trustworthy. Yet, we know little about the characteristics of explanations that result in these perceptions. Focusing on collaborative subjective decision-making, using the context of subtle sexism, where explanations can surface new interpretations, we conducted a user study (N=20) to explore the structural and content characteristics that affect perceptions of human and AI-generated verbal (text and audio) explanations. We find four groups of characteristics ( Tone, Grammatical Elements, Argumentative Sophistication and Relation to User ), and that the effect of these characteristics on the perception of explanations for subtle sexism depends on the perceived author. Thus, we also identify which explanation characteristics participants use to identify the author of an explanation. Demonstrating the relationship between these characteristics and explanation perceptions, we present a categorized set of characteristics that system builders can leverage to produce the appropriate perception of an explanation for various sensitive contexts. We also highlight human perception biases and associated issues resulting from these perceptions.

Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making Process

AI is anticipated to enhance human decision-making in high-stakes domains like aviation, but adoption is often hindered by challenges such as inappropriate reliance and poor alignment with users' decision-making. Recent research suggests that a core underlying issue is the recommendation-centric design of many AI systems, i.e., they give end-to-end recommendations and ignore the rest of the decision-making process. Alternative support paradigms are rare, and it remains unclear how the few that do exist compare to recommendation-centric support. In this work, we aimed to empirically compare recommendation-centric support to an alternative paradigm, continuous support, in the context of diversions in aviation. We conducted a mixed-methods study with 32 professional pilots in a realistic setting. To ensure the quality of our study scenarios, we conducted a focus group with four additional pilots prior to the study. We found that continuous support can support pilots' decision-making in a forward direction, allowing them to think more beyond the limits of the system and make faster decisions when combined with recommendations, though the forward support can be disrupted. Participants' statements further suggest a shift in design goal away from providing recommendations, to supporting quick information gathering. Our results show ways to design more helpful and effective AI decision support that goes beyond end-to-end recommendations.

Research on Digitalization of Personnel Archives to Address the Challenges of University Human Resource Management

Currently, there are several issues within human resource management in universities, such as mismatches between personnel and positions, challenges in quantifying performance evaluations, and the frequent departure of top talent. These problems are often linked to the underutilization of personnel archives. By integrating big data principles into the digitization of personnel archives at universities, we can enhance the "intelligent" aspects of human resource management and decision-making. This approach enables science-based management through digital quantification, fully leveraging the potential of university human resources. The article delves into the digitization of personnel archives using big data principles to address the challenges faced by university human resource departments and to foster the advancement and progress of various university initiatives.

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization

Abstract Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. The associated notebooks allow to reproduce the reported analyses and apply them to other systems.2

Deep-Learning-Based Automated Building Construction Progress Monitoring for Prefabricated Prefinished Volumetric Construction

Prefabricated prefinished volumetric construction (PPVC) is a relatively new technique that has recently gained popularity for its ability to improve flexibility in scheduling and resource management. Given the modular nature of PPVC assembly and the large amounts of visual data amassed throughout a construction project today, PPVC building construction progress monitoring can be conducted by quantifying assembled PPVC modules within images or videos. As manually processing high volumes of visual data can be extremely time consuming and tedious, building construction progress monitoring can be automated to be more efficient and reliable. However, the complex nature of construction sites and the presence of nearby infrastructure could occlude or distort visual data. Furthermore, imaging constraints can also result in incomplete visual data. Therefore, it is hard to apply existing purely data-driven object detectors to automate building progress monitoring at construction sites. In this paper, we propose a novel 2D window-based automated visual building construction progress monitoring (WAVBCPM) system to overcome these issues by mimicking human decision making during manual progress monitoring with a primary focus on PPVC building construction. WAVBCPM is segregated into three modules. A detection module first conducts detection of windows on the target building. This is achieved by detecting windows within the input image at two scales by using YOLOv5 as a backbone network for object detection before using a window detection filtering process to omit irrelevant detections from the surrounding areas. Next, a rectification module is developed to account for missing windows in the mid-section and near-ground regions of the constructed building that may be caused by occlusion and poor detection. Lastly, a progress estimation module checks the processed detections for missing or excess information before performing building construction progress estimation. The proposed method is tested on images from actual construction sites, and the experimental results demonstrate that WAVBCPM effectively addresses real-world challenges. By mimicking human inference, it overcomes imperfections in visual data, achieving higher accuracy in progress monitoring compared to purely data-driven object detectors.

Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference.

Sensors are the key to environmental monitoring, which impart benefits to smart cities in many aspects, such as providing real-time air quality information to assist human decision-making. However, it is impractical to deploy massive sensors due to the expensive costs, resulting in sparse data collection. Therefore, how to get fine-grained data measurement has long been a pressing issue. In this article, we aim to infer values at nonsensor locations based on observations from available sensors (termed spatiotemporal inference), where capturing spatiotemporal relationships among the data plays a critical role. Our investigations reveal two significant insights that have not been explored by previous works. First, data exhibit distinct patterns at both long- and short-term temporal scales, which should be analyzed separately. Second, short-term patterns contain more delicate relations, including those across spatial and temporal dimensions simultaneously, while long-term patterns involve high-level temporal trends. Based on these observations, we propose to decouple the modeling of short- and long-term patterns. Specifically, we introduce a joint spatiotemporal graph attention network to learn the relations across space and time for short-term patterns. Furthermore, we propose a graph recurrent network with a time skip strategy to alleviate the gradient vanishing problem and model the long-term dependencies. Experimental results on four public real-world datasets demonstrate that our method effectively captures both long- and short-term relations, achieving state-of-the-art performance against existing methods.

Investigating immersion and migration decisions for agent-based modelling: Acautionary tale.

Agent-based modelling provides an appealing methodological choice for simulating human behaviour and decisions. The currently dominant approaches based on static transition rates or unverified assumptions are restrictive, and could be enhanced with insights from cognitive experiments on actual decision making. Here, one common concern is that standard surveys or experiments may lack ecological validity, limiting the extent to which research findings can be generalised to real-life settings. For complex, highly emotive decision-making scenarios, such as those related to irregular migration, the typically used short, methodical survey questions may not appropriately map onto complex real-world decisions of interest. Immersive contexts may offer more accurate representations of reality, potentially enhancing the usefulness of experimental information in multi-disciplinary modelling endeavours. This preregistered study, aimed directly at examining the effect of immersion on risk-taking in the context of migration decisions, and indirectly at informing a multi-disciplinary construction of an agent-based model of migration, presents a choice-based interactive fiction game in which players make migration decisions to advance through a story. (N = 1000 Prolific users) took part in one of four experimental conditions, three involving different renditions of the game attempting to create immersion, with the last condition presenting the decisions in standard survey format. Although addressing the lack of ecological validity in survey data is important for improving agent-based modelling methodology, the experimental design used to tackle this issue, while responding directly to modelling needs, proved too complex. The created experimental conditions ended up too distinct from each other, involving stimuli that differed in quantity and content. This introduced several unintended and uncontrolled confounds, making it impossible to meaningfully interpret the results of this experiment on its own. Our results act as a cautionary tale for agent-based modellers, highlighting that the modelling needs should not override the principles of experimental design, and provide motivation for more rigorous research on this topic.

Hybrid Artificial Intelligence Strategies for Drone Navigation

Objective: This paper describes the development of hybrid artificial intelligence strategies for drone navigation. Methods: The navigation module combines a deep learning model with a rule-based engine depending on the agent state. The deep learning model has been trained using reinforcement learning. The rule-based engine uses expert knowledge to deal with specific situations. The navigation module incorporates several strategies to explain the drone decision based on its observation space, and different mechanisms for including human decisions in the navigation process. Finally, this paper proposes an evaluation methodology based on defining several scenarios and analyzing the performance of the different strategies according to metrics adapted to each scenario. Results: Two main navigation problems have been studied. For the first scenario (reaching known targets), it has been possible to obtain a 90% task completion rate, reducing significantly the number of collisions thanks to the rule-based engine. For the second scenario, it has been possible to reduce 20% of the time required to locate all the targets using the reinforcement learning model. Conclusions: Reinforcement learning is a very good strategy to learn policies for drone navigation, but in critical situations, it is necessary to complement it with a rule-based module to increase task success rate.

3.F. Round table: Legal action & capacity building: essential for climate action to protect and promote public health

Abstract The climate crisis is the single biggest threat to global health, peace and security, a crisis multiplier, and a significant driver of health inequalities within and between countries. In many areas of public health policy, legal action and litigation have delivered significant and long-lasting impacts. Court action is a strong domain of legal policy and practice to address climate change and related air pollution in many countries, and increasingly in international courts and tribunals. Examples include the 2021 landmark UK Coroners Report on the asthma-related death of Ella Kissi-Debrah, the 2023 UN General Assembly Request for an Advisory Opinion from the International Court of Justice on states’ obligations to address climate change, and the 2024 European Court of Human Rights decision in Klimanseniorinnen v. Switzerland. Climate litigation also creates opportunities for public scrutiny and debate by raising awareness of inaction or harm caused by governments or private sector polluters, generating wider community mobilisation for action. Since 2021, the EUPHA Law and Public Health Section, Faculty of Public Health (UK), Aletta Jacobs School of Public Health, Global Network for Academic Public Health, ASPHER and other partners have collaborated to build ethicolegal competence and capacity of public health professionals for climate action. Initiatives have also included research, advocacy, and resource development, and exploring opportunities for legal action and litigation, including at the International Conference on Strategic Litigation and Public Health (Liverpool, 2023). The roundtable will present key findings from an evaluation of the course on climate change organised by the ASPHER and the Global Consortium on Health and Climate Education (GCHCE), and learnings and feedback from the publication ‘From analysis to action: climate change litigation. A guide for public health professionals’, which was launched at the 16th European Public Health Conference. The expert panel will also reflect on whether the nature of current educational materials, means and methods in curriculum and training initiatives are effective, appropriate, adequate, and sufficient. Should climate change be part of One Health and Planetary Health programmes rather than a distinct entity? What is the role of, and what are the opportunities for, public health professionals to be engaged in climate litigation? What are the gaps and opportunities through strategic networking with environmental and legal experts, and affected communities? The roundtable will be interactive and invite input and insights from the participants to explore opportunities to further advance the agenda. Key messages • Public health professionals have a key and increasing role in climate law and litigation: interdisciplinary collaboration is essential. • Public health professionals have multiple avenues for capacity building on opportunities to address climate and environmental impacts on public health through law. Speakers/Panelists David Patterson University of Groningen, Groningen, Netherlands Farhang Tahzib Faculty of Public Health, Haywards heath, UK Liz Green Public Health Wales, Wrexham, UK Ana-Catarina Pinho-Gomes University College London, London, UK

6.Q. Round table: Artificial Intelligence in healthcare: navigating ethically with equity and workforce empowerment

Abstract Background Artificial Intelligence (AI) is emerging as a pivotal technology with vast promises for healthcare. However, integrating AI into clinical and public health settings must be cautiously approached to ensure that it does not inadvertently exacerbate existing health system problems. The healthcare workers, already under severe stress, could view AI as a threat to job security rather than as a support mechanism. Moreover, past experiences with digital transformations, such as Electronic Health Records, have shown that technological integration can sometimes increase rather than decrease the burden on healthcare workers, leading to burnout and dissatisfaction, and thus worsening the healthcare workforce crisis. Furthermore, equity and ethical considerations are paramount in the deployment of AI in healthcare. Data privacy, patient consent, and algorithmic bias must be addressed to ensure that AI applications are designed to support and enhance human decision-making that is sensitive to the social determinants of health and accountable to equity and social inclusion, to the needs and rights of the healthcare workforce, and the dignity of the patients and populations. While AI presents significant opportunities for health systems and healthcare workers, there is a lack of knowledge, evidence-based policies, and ethical frameworks that support equitable and human-centred approaches to AI implementation. Objectives This round table workshop aligns three major public health challenges: the integration of AI in health systems, the global healthcare workforce crisis, and the improvement of equity and equality. It critically explores capacity building for AI, equity in AI implementation, and regulatory measures for ethical and responsible AI deployment, addressing the following major questions: What are the critical impacts of AI on the healthcare workforce? How can the healthcare workforce be effectively upskilled and supported to adapt to the changes brought by AI technologies? What regulatory frameworks and governance models are necessary to ensure AI’s safe and ethical implementation in healthcare, that is also sensitive to equity, gender equality and the needs of minority groups? Finally, what actionable steps and leadership can public health take to implement AI technologies while addressing the healthcare workforce needs, equity issues, and ethical guidelines? The panellists will illuminate these questions from different disciplinary approaches, helping us to disentangle complexity and to build capacity for evidence-based and socially inclusive AI policies. The workshop contributes to better understand the risks and benefits of AI. It seeks to advance knowledge exchange of good practice experiences and effective implementation. Key messages • The effects of AI on the healthcare workforce must be monitored and strategies adapted to mitigate the healthcare workforce crisis and to upskill and empower healthcare workers. • There is a need for human-centred and ethically responsive AI implementation and governance measures that support equity, gender equality, and diversity in healthcare settings. Speakers/Panelists Abi Sriharan Schulich School of Business York University, York, Canada Kasia Czabanowska Maastricht University, Maastricht, Netherlands Bernadette Kumar Migration Health Unit, Norwegian Institute of Public Health, Oslo, Norway Marius-Ionuț Ungureanu Babeș-Bolyai University, Cluj-Napoca, Romania Farhang Tahzib Faculty of Public Health, Haywards heath, UK

For Me or Against Me? Reactions to AI (vs. Human) Decisions That Are Favorable or Unfavorable to the Self and the Role of Fairness Perception.

Public reactions to algorithmic decisions often diverge. While high-profile media coverage suggests that the use of AI in organizational decision-making is viewed as unfair and received negatively, recent survey results suggest that such use of AI is perceived as fair and received positively. Drawing on fairness heuristic theory, the current research reconciles this apparent contradiction by examining the roles of decision outcome and fairness perception on individuals' attitudinal (Studies 1-3, 5) and behavioral (Study 4) reactions to algorithmic (vs. human) decisions. Results from six experiments (N = 2,794) showed that when the decision was unfavorable, AI was perceived as fairer than human, leading to a less negative reaction. This heightened fairness perception toward AI is shaped by its perceived unemotionality. Furthermore, reminders about the potential biases of AI in decision-making attenuate the differential fairness perception between AI and human. Theoretical and practical implications of the findings are discussed.

Increasing transparency of computer-aided detection impairs decision-making in visual search.

Recent developments in artificial intelligence (AI) have led to changes in healthcare. Government and regulatory bodies have advocated the need for transparency in AI systems with recommendations to provide users with more details about AI accuracy and how AI systems work. However, increased transparency could lead to negative outcomes if humans become overreliant on the technology. This study investigated how changes in AI transparency affected human decision-making in a medical-screening visual search task. Transparency was manipulated by either giving or withholding knowledge about the accuracy of an 'AI system'. We tested performance in seven simulated lab mammography tasks, in which observers searched for a cancer which could be correctly or incorrectly flagged by computer-aided detection (CAD) 'AI prompts'. Across tasks, the CAD systems varied in accuracy. In the 'transparent' condition, participants were told the accuracy of the CAD system, in the 'not transparent' condition, they were not. The results showed that increasing CAD transparency impaired task performance, producing an increase in false alarms, decreased sensitivity, an increase in recall rate, and a decrease in positive predictive value. Along with increasing investment in AI, this research shows that it is important to investigate how transparency of AI systems affect human decision-making. Increased transparency may lead to overtrust in AI systems, which can impact clinical outcomes.

How can Artificial Intelligence Teammates Know What Humans Want? Using Eye-Tracking Data to Infer Human Preferences in Game-Theoretic Decision Tasks

For human-agent teams, it is as important for agents to have models of their human teammates as it is for humans to have models of their agent teammates. However, approaches to knowledge elicitation have wrestled with the problem of capturing human knowledge when much of it is tacit and difficult to verbalize. By observing the choices that people make under different conditions we can infer the choice structure they appear to be following. In this way, observations could allow the agent to predict its human team-mates’ choices and actions. We report an experiment in which eye-tracking is used to capture choices in a constrained task and then develop a decision model that can replicate aspects of these choices. We propose that such a model can support human-agent teams by enabling inferences of some aspects of human decision-making.

Explainability increases trust resilience in intelligent agents.

Even though artificial intelligence (AI)-based systems typically outperform human decision-makers, they are not immune to errors, leading users to lose trust in them and be less likely to use them again-a phenomenon known as algorithm aversion. The purpose of the present research was to investigate whether explainable AI (XAI) could function as a viable strategy to counter algorithm aversion. We conducted two experiments to examine how XAI influences users' willingness to continue using AI-based systems when these systems exhibit errors. The results showed that, following the observation of algorithms erring, the inclination of users to delegate decisions to or follow advice from intelligent agents significantly decreased compared to the period before the errors were revealed. However, the explainability effectively mitigated this decline, with users in the XAI condition being more likely to continue utilizing intelligent agents for subsequent tasks after seeing algorithms erring than those in the non-XAI condition. We further found that the explainability could reduce users' decision regret, and the decrease in decision regret mediated the relationship between the explainability and re-use behaviour. These findings underscore the adaptive function of XAI in alleviating negative user experiences and maintaining user trust in the context of imperfect AI.

Ethical Concerns Upon Artificial Intelligence Empowered Human Resource Management: A Qualitative Study among Middle-level Managers from Beijing Technology Companies

The evolution of artificial intelligence (AI) in organizational management has significantly enhanced operational efficiency. However, it has introduced ethical challenges in human resource management. Between January to March 2024, this study conducted 21 semi-structured interviews with middle-level managers from high-tech companies in Beijing. Through word frequency analysis, the study found that key topics among the managers were “company,” “data,” “system,” and “problem,” with “AI” frequently recurring in the discussions. Sentiment analysis revealed generally favorable attitudes toward AI in human resource management (AI-HRM), along with nuanced emotional expressions such as inquiry, introspection, recommendation, challenge, adaptation, resistance, and indifference. The sentiment distribution of keywords aligned with topic trends. Thematic analysis identified key ethical concerns in AI-HRM, including issues related to data collection and utilization, human versus machine decision-making, quantitative versus qualitative assessment methodologies, the balance between fairness and efficiency, the need for trustworthy, explainable, and transparent AI, and the oversight of AI-HRM. This study contributes to the ethical investigation of AI-HRM from the perspective of middle-level managers, highlighting themes that are critical for understanding the theory, application, and future development of AI-HRM.

How mariculture expansion is dewilding the ocean and its inhabitants.

The world's oceans are largely free from intensive farming, but momentum to intensify and expand mariculture-the cultivation of aquatic organisms in the ocean-is growing. Despite optimism that mariculture will create economic and nutritional benefits for humans, it can also generate a host of risks, including environmental degradation, harms to wildlife integrity and welfare, captivity effects, and shifts in how humans view the nonhuman world. Collectively, we refer to these four types of risks as "dewilding." In this systematic review, we searched Scopus and Web of Science for recent literature documenting mariculture's dewilding impacts to organize and collate this evidence under one unified framework. We find that mariculture's dewilding impacts are consistently documented, though often in isolation, and that captivity and conceptual dewilding impacts are recognized as potential harms far less than impacts on the environment and wildlife. Future work examining mariculture's dewilding impacts will be paramount to guiding human decision-making and activity going forward.

Intelligent Borders: Exploring the Suitability of Artificial Intelligence Systems in Refugee Status Determination Under International Law

Abstract This article assesses the potential use of Artificial Intelligence (AI) in Refugee Status Determination, exploring how AI systems, including biometrics, predictive analytics, and emotion recognition, could support or replace human decision-making in determining refugee status. While AI could speed up and improve the objectivity of Refugee Status Determination procedures, the article raises concerns about its limitations in assessing the subjective fear of persecution, a critical element of refugee claims. The inability of AI systems to fully account for emotional and personal nuances leads to the possibility of discriminatory practices, particularly when AI is used without human oversight. The study highlights how AI could exacerbate power imbalances between asylum-seekers and States, transforming these technologies into tools for anti-immigration policies. It therefore calls for caution in the use of AI in Refugee Status Determination procedures and stresses that human interaction remains essential for the fair assessment of refugee claims. This article concludes that while AI can assist in certain aspects of the Refugee Status Determination procedures, its full implementation without careful legal and human rights considerations poses significant risks.