Qiyang Wan, Chengzhi Gao, Ruiping Wang, Xilin Chen
Institute of Computing Technology, Chinese Academy of Sciences
University of Chinese Academy of Sciences
This page summarizes the related surveys of A Survey on Interpretability in Visual Recognition, organized into three main categories:
XAI techniques, classification methods, evaluation metrics, and future challenges for various AI models
XAI research in visual tasks, visualization techniques, architectures, multimodal models, and reinforcement learning
Applications of XAI in medical imaging, industrial manufacturing, smart cities, and cybersecurity
Summary and comparison of recent XAI surveys on Generic AI Models
| Type | Reference | Year | Literature Coverage | Description |
|---|---|---|---|---|
|
XAI Surveys on Generic AI Models
|
abusitta2024survey | 2024 | 2018-2024 | Propose a classification of XAI techniques, emphasizing applications in cybersecurity and future challenges |
| schwalbe2024comprehensive | 2024 | 2017-2024 | Propose a unified taxonomy of XAI methods and provide use-case-oriented insights | |
| hassija2024interpreting | 2024 | 2016-2024 | Review XAI models, evaluation metrics, challenges, and trends to enhance transparency | |
| mersha2024explainable | 2024 | 2015-2024 | Deliver a review of XAI, examining methods, applications, and principles to enhance transparency | |
| mazhar2024survey | 2024 | 2018-2023 | Survey XAI methods for deep learning, investigating techniques, tools, and future research directions | |
| chander2024toward | 2024 | 2017-2023 | Discuss trustworthy AI, focusing on robustness and explainability to enhance safety and reliability | |
| chamola2023review | 2023 | 2019-2023 | Evaluate trustworthy and explainable AI, focusing on transparency and reliability across industries | |
| yang2023survey | 2023 | 2017-2023 | Review XAI research, focusing on key areas, taxonomies, applications, and future directions | |
| saranya2023systematic | 2023 | 2020-2022 | Conduct a systematic review of XAI applications across various fields and suggest directions for future research | |
| saeed2023explainable | 2023 | 2017-2022 | Conduct a systematic meta-survey of XAI challenges and future research across development phases | |
| samek2023explainable | 2023 | 2017-2022 | Introduce XAI techniques for deep neural networks, emphasizing challenges and future directions | |
| dwivedi2023explainable | 2023 | 2017-2021 | Survey XAI techniques, classify approaches, and guide framework selection for interpretable AI systems | |
| holzinger2022explainable | 2022 | 2019-2022 | Present a concise overview of 17 key XAI methods for beginners | |
| saleem2022explaining | 2022 | 2019-2022 | Review global interpretation methods in XAI, highlighting strengths, weaknesses, and future opportunities | |
| ding2022explainability | 2022 | 2016-2022 | Discuss XAI principles, taxonomy, evaluation, and challenges, suggesting future research directions | |
| ras2022explainable | 2022 | 2014-2022 | Guide readers through explainable deep learning, discussing key methods, evaluations, and future directions | |
| rudin2022interpretable | 2022 | 2013-2022 | Identify 10 technical challenge areas and provide historical and background context | |
| hanif2021survey | 2021 | 2017-2021 | Survey XAI techniques, emphasizing the need for transparent and responsible AI to build trust | |
| zhang2021survey | 2021 | 2015-2021 | Propose a taxonomy of neural network interpretability based on engagement, explanation type, and focus | |
| samek2021explaining | 2021 | 2014-2021 | Offer an overview of post-hoc explanations, evaluate interpretability methods, and demonstrate XAI applications | |
| islam2021explainable | 2021 | 2018-2020 | Analyze XAI methods for credit default prediction, compare advantages, and suggest future research | |
| fan2021interpretability | 2021 | 2014-2020 | Propose a taxonomy and discuss applications and future directions | |
| das2020opportunities | 2020 | 2017-2020 | Deliver an overview of XAI techniques, including taxonomy, methods, principles, and evaluation | |
| huang2020survey | 2020 | 2015-2020 | Review research on making DNNs safe, concentrating on verification, testing, attacks, and interpretability | |
| arrieta2020explainable | 2020 | 2007-2020 | Explore the importance of explainability in AI and present a taxonomy of XAI techniques | |
| ferreira2020people | 2020 | 2018-2019 | Explore AI explainability from CS and HCI perspectives, concentrating on goals, recipients, and context |
This table summarizes XAI survey literature on Generic AI Models from 2020-2024, covering 26 studies
Summary and comparison of recent XAI surveys on specific vision-related fields
| Type | Reference | Year | Literature Coverage | Description |
|---|---|---|---|---|
| Visual Task | gipivskis2024explainable | 2024 | 2017-2024 | Survey XAI in semantic segmentation, categorizing evaluation metrics and future challenges |
| poeta2023concept | 2023 | 2018-2023 | Review concept-based XAI methods, offering taxonomy, guidelines, and evaluations for the future | |
| bai2021explainable | 2021 | 2015-2021 | Introduce papers on explainable deep learning, efficiency, and robustness in pattern recognition | |
| Visualization | baniecki2024adversarial | 2024 | 2017-2024 | Survey adversarial attacks on XAI, outlining security challenges and suggesting future directions |
| alicioglu2022survey | 2022 | 2018-2021 | Review trends and challenges in visual analytics for XAI, focusing on deep learning model interpretation | |
| seifert2017visualizations | 2017 | 2014-2016 | Survey visualization methods for DNNs, focusing on insights gained in computer vision | |
| Architecture | fantozzi2024explainability | 2024 | 2017-2024 | Survey transformer explainability, categorizing by components, applications, and visualization |
| kashefi2023explainability | 2023 | 2021-2023 | Review explainability methods for vision transformers, categorizing approaches and evaluation criteria | |
| stassin2023explainability | 2023 | 2019-2023 | Evaluate XAI for vision transformers, highlighting challenges in metrics, convergence, and adaptation | |
| Multimodal | kazmierczak2025explainability | 2025 | 2017-2025 | Survey integration of foundation models with explainable AI in the vision domain |
| dang2024explainable | 2024 | 2017-2024 | Analyze recent advances in Multimodal XAI, focusing on methods, datasets, and evaluation metrics | |
| rodis2024multimodal | 2024 | 2016-2024 | Survey interpretability of MLLMs, categorizing evaluations and future directions | |
| sun2024review | 2024 | 2015-2024 | Review research on interpretability of MLLMs, focusing on challenges, metrics, and future directions | |
| Reinforcement Learning | milani2024explainable | 2024 | 2017-2024 | Survey XRL techniques, introduce a taxonomy, and outline challenges and future directions |
| glanois2024survey | 2024 | 2015-2024 | Survey interpretability approaches in reinforcement learning, focusing on inputs, models, and decisions | |
| dazeley2023explainable | 2023 | 2015-2023 | Introduce the Causal XRL Framework, unify XRL research, and explore future Broad-XAI development | |
| puiutta2020explainable | 2020 | 2017-2019 | Survey XRL methods and highlight the need for interdisciplinary human-centered explanations | |
| Others | hu2024interpretable | 2024 | 2021-2024 | Review explainable clustering methods, emphasizing transparency and ethics in high-stakes applications |
| schneider2024explainable | 2024 | 2019-2024 | Review XAI in generative AI, addressing challenges, categories, criteria, and future research directions | |
| burkart2021survey | 2021 | 2015-2021 | Survey explainable supervised learning methods, highlighting principles, methodologies, and directions | |
| li2020survey | 2020 | 2015-2020 | Survey explanation methods, focusing on data-driven and knowledge-aware approaches, and applications |
This table summarizes XAI survey literature on Vision-Related Fields from 2020-2025, covering 21 studies
Summary and comparison of recent XAI surveys on vision-related applications
| Type | Reference | Year | Literature Coverage | Description |
|---|---|---|---|---|
| Medical Imaging | bhati2024survey | 2024 | 2017-2024 | Survey interpretability and visualization techniques for deep learning in medical imaging |
| hou2024self | 2024 | 2017-2024 | Survey self-explainable AI for medical image analysis: methods, challenges, and future research | |
| klauschen2024toward | 2024 | 2015-2023 | Explore diagnostic pathology: classification, biomarker quantification, transparency, XAI solutions | |
| borys2023explainable | 2023 | 2020-2022 | Review non-saliency XAI methods in medical imaging for clinicians | |
| chaddad2023survey | 2023 | 2019-2022 | Survey XAI techniques, categorize challenges, and suggest future directions in medical imaging | |
| nazir2023survey | 2023 | 2017-2022 | Survey XAI for medical imaging diagnostics, analyze challenges, and propose future directions | |
| sheu2022survey | 2022 | 2018-2022 | Survey medical XAI: evaluations, case studies, human-machine collaboration, and the future | |
| teng2022survey | 2022 | 2019-2021 | Review interpretability in medical diagnosis: methods, applications, challenges, and future directions | |
| jin2022explainable | 2022 | 2016-2021 | Review interpretability in healthcare: methods, advantages, applications, and future directions | |
| tjoa2020survey | 2020 | 2015-2020 | Categorize AI interpretability approaches to guide cautious application in medical practices | |
| pocevivciute2020survey | 2020 | 2015-2020 | Survey XAI in digital pathology: techniques, uncertainty estimation, and cross-disciplinary insights | |
| Industry/Manufacturing | naqvi2024survey | 2024 | 2001-2023 | Survey ontology-based and semantic-based XAI for transparent AI decisions in manufacturing |
| alexander2024interrogative | 2024 | 2020-2022 | Survey explainable AI applications in manufacturing, highlight research, and propose future directions | |
| li2023survey | 2023 | 2016-2023 | Survey explainable anomaly detection: techniques, taxonomy, ethics, and guidance for practitioners | |
| ahmed2022artificial | 2022 | 2018-2021 | Survey AI and XAI methods in Industry 4.0 for autonomous decision-making and transparency | |
| Smart City | javed2023survey | 2023 | 2018-2023 | Survey XAI developments in smart cities, focusing on use cases, challenges, and research directions |
| kok2023explainable | 2023 | 2018-2023 | Examine XAI in IoT: transparent models, challenges, foresee future directions, and classify studies | |
| jagatheesaperumal2022explainable | 2022 | 2018-2022 | Study XAI in IoT: assess frameworks, security, IoMT, IIoT, IoCT, edge XAI, and future directions | |
| Cybersecurity | rjoub2023survey | 2023 | 2020-2023 | Survey XAI in cybersecurity: approaches, challenges, limitations, and future research directions |
| charmet2022explainable | 2022 | 2018-2022 | Survey XAI in cybersecurity: applications, security concerns, challenges, and future directions | |
| capuano2022explainable | 2022 | 2018-2022 | Conduct in-depth study on XAI in cybersecurity: applications, challenges, methods, and the future | |
| Others | hohl2024opening | 2024 | 2017-2024 | Perform systematic review of XAI methods, trends, and challenges in Remote Sensing |
| alizadehsani2024explainable | 2024 | 2020-2023 | Review XAI methods in drug discovery, identify challenges, applications, and future directions |
This table summarizes XAI survey literature on Vision Applications from 2020-2025, covering 23 studies