Artificial Intelligence Review最新文献

Unlocking the potential of deep learning in brain stroke prognosis: a systematic literature review 释放深度学习在脑中风预后中的潜力：系统的文献综述

IF 13.9 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-10-11 DOI: 10.1007/s10462-025-11353-0

Annas Barouhou, Laila Benhlima, Slimane Bah

{"title":"Unlocking the potential of deep learning in brain stroke prognosis: a systematic literature review","authors":"Annas Barouhou, Laila Benhlima, Slimane Bah","doi":"10.1007/s10462-025-11353-0","DOIUrl":"10.1007/s10462-025-11353-0","url":null,"abstract":"<div><p>Stroke remains a significant global health concern, necessitating accurate and timely prognosis to optimize patient care and outcomes. In recent years, deep learning, a subset of artificial intelligence, has emerged as a promising tool for enhancing stroke prognosis by leveraging its capability to analyze complex clinical and imaging data. This advancement has sparked a significant increase in research publications in this domain. Therefore, our objective in this systematic literature review (SLR) is to: systematically review and analyze the existing body of literature to identify key deep learning architectures, evaluate their performance against conventional prognosis methods, explore the range of clinical and neuroimaging data sources employed, and investigate the potential impact of deep learning on personalized stroke management. Our findings reveal that deep learning holds considerable promise in improving stroke prognosis accuracy, offering opportunities for more precise clinical decision-making. However, challenges related to data heterogeneity, interpretability, and clinical integration persist. We discuss these challenges and propose future directions to facilitate the successful integration of deep learning into routine stroke care. As the demand for precise stroke prognosis intensifies, this review serves as a valuable resource for researchers, clinicians, and policymakers alike, offering insights into the current state of deep learning applications in stroke prognosis and guiding efforts toward leveraging artificial intelligence to alleviate the burden of stroke on individuals and healthcare systems.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 12","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11353-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization of the composting process using artificial neural networks—a literature review 利用人工神经网络优化堆肥过程的文献综述

IF 13.9 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-10-11 DOI: 10.1007/s10462-025-11380-x

Bartosz Gręziak, Andrzej Białowiec

{"title":"Optimization of the composting process using artificial neural networks—a literature review","authors":"Bartosz Gręziak, Andrzej Białowiec","doi":"10.1007/s10462-025-11380-x","DOIUrl":"10.1007/s10462-025-11380-x","url":null,"abstract":"<div><p>Composting is a complex biological process, and due to the numerous variables affecting its course, it requires constant supervision and, depending on the needs, appropriate modifications. In particular, it is necessary to strive to ensure the quality of substrates, the elimination of possible contaminants, the efficient and inexpensive conduct of the process, and the fulfillment by the finished compost of the quality requirements allowing its use as a fertilizer or crop improvement agent. Therefore, new effective methods for composting optimization are needed. This paper reviews the state of the art on the use of artificial neural networks (ANN) in bio-waste composting with a special focus on applying machine learning tools. Artificial neural networks were characterized along with their division into different types, the basics of the composting process and legal requirements for bio-waste recycling were described. Different types of machine learning were compared with attention paid to the effectiveness of the tools used. Also, for further studies, the appropriate independent variables were proposed to be used in ANN designing. The presented examples of the application of ANN confirm the usefulness of this method, to solve the complexity of the composting issue, and the need for further research.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 12","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11380-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient mortality prediction for acute respiratory failure: a resource-constrained machine learning approach using MIMIC databases 急性呼吸衰竭的有效死亡率预测：使用MIMIC数据库的资源受限机器学习方法

IF 13.9 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-10-11 DOI: 10.1007/s10462-025-11387-4

Muhammad Talha Khan, Maryam Gulzar, Arshad Ali, Aamir Wali, Rida Amir

{"title":"Efficient mortality prediction for acute respiratory failure: a resource-constrained machine learning approach using MIMIC databases","authors":"Muhammad Talha Khan, Maryam Gulzar, Arshad Ali, Aamir Wali, Rida Amir","doi":"10.1007/s10462-025-11387-4","DOIUrl":"10.1007/s10462-025-11387-4","url":null,"abstract":"<div><p>Accurate prediction of mortality in Acute Respiratory Failure (ARF) patients at intensive care unit (ICU) admission can improve patient outcomes and resource management. However, ICU environments often face challenges like missing test results and limited resources. This study presents a complete pipeline for predicting ARF mortality, focusing on effective feature extraction, data imputation, and class imbalance handling. Key preprocessing steps include iterative imputation for missing data and upsampling techniques like SMOTE and deep learning-based generators. Using the MIMIC-III and MIMIC-IV databases, logistic regression, random forest, extreme gradient boosting, and neural networks were tested. Findings demonstrate that neural networks, along with ensemble methods, achieved high sensitivity and <span>(hbox {F}_beta )</span> scores, which are essential for accurate mortality predictions. Notably, when class distribution was balanced, the models performed equally well on specificity and sensitivity. SMOTE proved particularly effective in addressing class imbalance, suggesting that advanced upsampling methods like GANs could further enhance prediction accuracy without reducing dataset size. </p><h3>Graphical abstract</h3><p>This graphical abstract of the work that illustrates that a patient is admitted to the hospital, admission to the ICU is determined, the test results of the first 24 hours are collected, missing parameters are imputed, the data are normalized and a machine learning model is applied to predict mortality outcomes.</p><div><figure><div><div><picture><source><img></source></picture></div><div><p>Graphical abstract illustrating the process</p></div></div></figure></div></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 12","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11387-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CTWA: a novel incremental deep learning-based intrusion detection method for the Internet of Things CTWA：一种新的基于增量深度学习的物联网入侵检测方法

IF 13.9 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-10-08 DOI: 10.1007/s10462-025-11358-9

Haizhen Wang, Yutong Yang, Pan Tan

{"title":"CTWA: a novel incremental deep learning-based intrusion detection method for the Internet of Things","authors":"Haizhen Wang, Yutong Yang, Pan Tan","doi":"10.1007/s10462-025-11358-9","DOIUrl":"10.1007/s10462-025-11358-9","url":null,"abstract":"<div><p>Class incremental learning aims to learn new courses in an incremental manner without forgetting the categories previously learned. A novel incremental Internet of Things (IoT) intrusion detection method CTWA based on Convolutional Autoencoder (CAE) and Temporal Convolutional Network (TCN) is proposed to address the issues of insufficient generalization ability, high computational resources, and redundant features in class incremental learning. This method first completes the training of the CAE-TCN module, extracts and concatenates local features of data samples through CAE and TCN, and then initializes the incremental learning module. Residual modules are added to the CAE to improve the training effect of the model and avoid gradient vanishing problems. The CAE-TCN module shares lower-level feature representations through task-specific layers in incremental learning module. It distinguishes between old and new tasks using Gaussian distribution, and applies Weight alignment (WA) techniques between task heads to ensure that learning the new task does not result in forgetting the knowledge of the old tasks. Ultimately, the outputs of both new and old tasks are weighted and fused to ensure the optimal classification result. Additionally, a loss function combining cross-entropy loss and label smoothing loss is used to enhance the model’s performance. We conducted experiments on two datasets. The experimental results on CICIoT2023 dataset demonstrate that the proposed model excels in terms of accuracy, precision, recall, and F1-Score, achieving 0.9643, 0.9659, 0.9643, and 0.9645, respectively. With 40 training epochs, the model’s runtime is 789.58 s, which is higher than most comparison models, but the accuracy is significantly improved. The proposed method can effectively distinguishes between different known and unknown types of attacks, highlighting its potential applications in the field of cybersecurity.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 12","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11358-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Securing (vision-based) autonomous systems: taxonomy, challenges, and defense mechanisms against adversarial threats 保护（基于视觉的）自治系统：针对敌对威胁的分类、挑战和防御机制

IF 13.9 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-10-08 DOI: 10.1007/s10462-025-11373-w

Alvaro Lopez Pellicer, Plamen Angelov, Neeraj Suri

{"title":"Securing (vision-based) autonomous systems: taxonomy, challenges, and defense mechanisms against adversarial threats","authors":"Alvaro Lopez Pellicer, Plamen Angelov, Neeraj Suri","doi":"10.1007/s10462-025-11373-w","DOIUrl":"10.1007/s10462-025-11373-w","url":null,"abstract":"<div><p>The rapid integration of computer vision into Autonomous Systems (AS) has introduced new vulnerabilities, particularly in the form of adversarial threats capable of manipulating perception and control modules. While multiple surveys have addressed adversarial robustness in deep learning, few have systematically analyzed how these threats manifest across the full stack and life-cycle of AS. This review bridges that gap by presenting a structured synthesis that spans both, foundational vision-centric literature and recent AS-specific advances, with focus on digital and physical threat vectors. We introduce a unified framework mapping adversarial threats across the AS stack and life-cycle, supported by three novel analytical matrices: the <i>Life-cycle–Attack Matrix</i> (linking attacks to data, training, and inference stages), the <i>Stack–Threat Matrix</i> (localizing vulnerabilities throughout the autonomy stack), and the <i>Exposure–Impact Matrix</i> (connecting attack exposure to AI design vulnerabilities and operational consequences). Drawing on these models, we define holistic requirements for effective AS defenses and critically appraise the current landscape of adversarial robustness. Finally, we propose the <i>AS-ADS</i> scoring framework to enable comparative assessment of defense methods in terms of their alignment with the practical needs of AS, and outline actionable directions for advancing the robustness of vision-based autonomous systems.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 12","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11373-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic review of bias detection methods for non-English word embeddings and language models 非英语词嵌入和语言模型的偏见检测方法的系统综述

IF 13.9 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-10-08 DOI: 10.1007/s10462-025-11375-8

Alexandre Puttick, Catherine Ikae, Carlotta Rigotti, Eduard Fosch-Villaronga, Mark W. Kharas, Roger A. Søraa, Mascha Kurpicz-Briki

{"title":"A systematic review of bias detection methods for non-English word embeddings and language models","authors":"Alexandre Puttick, Catherine Ikae, Carlotta Rigotti, Eduard Fosch-Villaronga, Mark W. Kharas, Roger A. Søraa, Mascha Kurpicz-Briki","doi":"10.1007/s10462-025-11375-8","DOIUrl":"10.1007/s10462-025-11375-8","url":null,"abstract":"<div><p>Biases in applications of machine learning and artificial intelligence are a major limitation of these applications. Stereotypes of the society are reflected in different types of applications, including image generation, machine translation or CV ranking. This is in particular also the case for language models and word embeddings, encoding human language as mathematical vectors. Research addressing the challenging problem of detection (and mitigation) of the bias in these embeddings is often conducted for the English language. However, the stereotypes encoded can be language dependent and impacted by a cultural environment. Thus, dedicated research efforts for languages other than English are required. In this paper, we conduct a systematic literature review to identify and compare existing bias detection methods for non-English word embeddings and language models. In an interdisciplinary team we examine the technical aspects, as well as the definitions of bias used by researchers in the field. Based on our findings, we outline a research plan for making bias detection in the field of NLP more inclusive for languages other than English.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 12","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11375-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning diversified representations for visual abstract reasoning 学习视觉抽象推理的多样化表征

IF 13.9 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-10-08 DOI: 10.1007/s10462-025-11372-x

Kai Zhao, Yao Zhu, Bailu Si

{"title":"Learning diversified representations for visual abstract reasoning","authors":"Kai Zhao, Yao Zhu, Bailu Si","doi":"10.1007/s10462-025-11372-x","DOIUrl":"10.1007/s10462-025-11372-x","url":null,"abstract":"<div><p>Learning effective representations suitable for decision making in high-level cognitive space is crucial for visual abstract reasoning tasks. The visual system of the mammalian brain is organized into parallel networks that can be roughly classified in dichotomy as the dorsal and ventral streams. How do parallel networks learn efficient representations for cognitive tasks is still an elusive question. We propose the Information Competition Learning Network (ICNet) within a mutual information-constrained framework to learn diversified representations for visual abstract reasoning tasks. ICNet comprises a representation learning module and a rule extractor module. The representation learning module learns two complementary sets of representation under different constraints. These two sets compete to prevent from learning what the other has learned, thereby minimizing mutual predictability. Subsequently, these sets are combined synergistically and relayed to the rule extractor module, where discrete abstract rules are formed to predict the correct option. Empirical experiments consistently show that ICNet achieves superior results across several visual abstract reasoning datasets. Additionally, in Out-of-Distribution relationship reasoning benchmarks, ICNet demonstrates robust generalization ability.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 12","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11372-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Empowering scientific discovery with explainable small domain-specific and large language models 通过可解释的小型领域特定模型和大型语言模型增强科学发现的能力

IF 13.9 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-10-08 DOI: 10.1007/s10462-025-11365-w

Hengjie Yu, Yizhi Wang, Tao Cheng, Yan Yan, Kenneth A. Dawson, Sam F. Y. Li, Yefeng Zheng, Yaochu Jin

{"title":"Empowering scientific discovery with explainable small domain-specific and large language models","authors":"Hengjie Yu, Yizhi Wang, Tao Cheng, Yan Yan, Kenneth A. Dawson, Sam F. Y. Li, Yefeng Zheng, Yaochu Jin","doi":"10.1007/s10462-025-11365-w","DOIUrl":"10.1007/s10462-025-11365-w","url":null,"abstract":"<div><p>As artificial intelligence (AI) increasingly integrates into scientific research, explainability has become a cornerstone for ensuring reliability and innovation in discovery processes. This review offers a forward-looking integration of explainable AI (XAI)-based research paradigms, encompassing small domain-specific models, large language models (LLMs), and agent-based large-small model collaboration. For domain-specific models, we introduce a knowledge-oriented taxonomy categorizing methods into knowledge-agnostic, knowledge-based, knowledge-infused, and knowledge-verified approaches, emphasizing the balance between domain knowledge and innovative insights. For LLMs, we examine three strategies for integrating domain knowledge—prompt engineering, retrieval-augmented generation, and supervised fine-tuning—along with advances in explainability, including local, global, and conversation-based explanations. We also envision future agent-based model collaborations within automated laboratories, stressing the need for context-aware explanations tailored to research goals. Additionally, we discuss the unique characteristics and limitations of both explainable small domain-specific models and LLMs in the realm of scientific discovery. Finally, we highlight methodological challenges, potential pitfalls, and the necessity of rigorous validation to ensure XAI’s transformative role in accelerating scientific discovery and reshaping research paradigms.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 12","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11365-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI in motion: a systematic review of artificial intelligence-driven virtual assistants for physical activity promotion and their comparison with traditional strategies 运动中的人工智能：对人工智能驱动的体育活动促进虚拟助手的系统回顾及其与传统策略的比较

IF 13.9 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-10-06 DOI: 10.1007/s10462-025-11361-0

Alice Montelaghi, Andrea Ciorciari, Roberto Roklicer, Gregor Jurak, Attilio Carraro

{"title":"AI in motion: a systematic review of artificial intelligence-driven virtual assistants for physical activity promotion and their comparison with traditional strategies","authors":"Alice Montelaghi, Andrea Ciorciari, Roberto Roklicer, Gregor Jurak, Attilio Carraro","doi":"10.1007/s10462-025-11361-0","DOIUrl":"10.1007/s10462-025-11361-0","url":null,"abstract":"<div><p>Physical inactivity remains a major public health concern globally, prompting the need for scalable, cost-effective interventions. Artificial Intelligence-driven Virtual Assistants (AIVAs) such as chatbots and virtual agents have emerged as novel methods to promote physical activity (PA), yet their effectiveness compared to traditional strategies remains unclear. This systematic review aimed at examining the characteristics, strategies, and effectiveness of AIVAs in promoting PA in adults and to compare them with traditional interventions. A systematic search of Scopus, Web of Science, PubMed, and Cochrane was conducted through May 2025. Eight interventional studies that employed AIVAs targeting PA were included. Risk of bias was assessed using ROBINS-I and RoB 2 tools. Intervention characteristics, outcomes, and behavioral strategies were extracted and synthesized. AIVAs were found to incorporate established behavior change techniques such as goal setting, feedback, and motivational support. Several studies demonstrated positive effects on PA metrics such as step counts and moderate to vigorous PA, though results were heterogeneous. Engagement and usability were generally high, particularly in interventions incorporating relational features. Compared to traditional interventions, AIVAs offered advantages in scalability and user autonomy but often lacked rigorous designs and long-term evaluation. AIVAs show promise as complementary tools for PA promotion, potentially overcoming scalability barriers associated with human-delivered programs. However, future research should prioritize methodologically robust designs, long-term assessments, and hybrid models that integrate both human and AI elements.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 12","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11361-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI apology: a critical review of apology in AI systems 人工智能道歉：对人工智能系统中道歉的批判性回顾

IF 13.9 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-10-06 DOI: 10.1007/s10462-025-11305-8

Hadassah Harland, Richard Dazeley, Hashini Senaratne, Peter Vamplew, Francisco Cruz, Bahareh Nakisa

{"title":"AI apology: a critical review of apology in AI systems","authors":"Hadassah Harland, Richard Dazeley, Hashini Senaratne, Peter Vamplew, Francisco Cruz, Bahareh Nakisa","doi":"10.1007/s10462-025-11305-8","DOIUrl":"10.1007/s10462-025-11305-8","url":null,"abstract":"<div><p>Apologies are a powerful tool used in human-human interactions to provide affective support, regulate social processes, and exchange information following a trust violation. The emerging field of AI apology investigates the use of apologies by artificially intelligent systems, with recent research suggesting how this tool may provide similar value in human-machine interactions. Until recently, contributions to this area were sparse, and these works have yet to be synthesised into a cohesive body of knowledge. This article provides the first synthesis and critical analysis of the state of AI apology research, focusing on studies published between 2020 and 2023. We derive a framework of attributes to describe five core elements of apology: outcome, interaction, offence, recipient, and offender. With this framework as the basis for our critique, we show how apologies can be used to recover from misalignment in human-AI interactions, and examine trends and inconsistencies within the field. Among the observations, we outline the importance of curating a human-aligned and cross-disciplinary perspective in this research, with consideration for improved system capabilities and long-term outcomes.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 12","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11305-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0