{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T04:43:21Z","timestamp":1789101801816,"version":"build-2803163510"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62476137"],"award-info":[{"award-number":["62476137"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postgraduate Research & Practice Innovation Program of Jiangsu Province","award":["KYCX23 1050"],"award-info":[{"award-number":["KYCX23 1050"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s10618-025-01111-9","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T12:43:06Z","timestamp":1748349786000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Data debiasing via causal diffusion model"],"prefix":"10.1007","volume":"39","author":[{"given":"Wenqiong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yikai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,27]]},"reference":[{"key":"1111_CR1","first-page":"364","volume":"35","author":"M Augustin","year":"2022","unstructured":"Augustin M, Boreiko V, Croce F, Hein M (2022) Diffusion visual counterfactual explanations. Adv Neural Inf Process Syst 35:364\u2013377","journal-title":"Adv Neural Inf Process Syst"},{"issue":"5","key":"1111_CR2","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1007\/s00778-021-00671-8","volume":"30","author":"A Balayn","year":"2021","unstructured":"Balayn A, Lofi C, Houben G-J (2021) Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems. VLDB J 30(5):739\u2013768","journal-title":"VLDB J"},{"key":"1111_CR3","doi-asserted-by":"crossref","unstructured":"Brempong EA, Kornblith S, Chen T, Parmar N, Minderer M, Norouzi M (2022) Denoising pretraining for semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4175\u20134186","DOI":"10.1109\/CVPRW56347.2022.00462"},{"key":"1111_CR4","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s10618-010-0190-x","volume":"21","author":"T Calders","year":"2010","unstructured":"Calders T, Verwer S (2010) Three naive bayes approaches for discrimination-free classification. Data Min Knowl Discov 21:277\u2013292","journal-title":"Data Min Knowl Discov"},{"key":"1111_CR5","first-page":"15077","volume":"33","author":"T Cemgil","year":"2020","unstructured":"Cemgil T, Ghaisas S, Dvijotham K, Gowal S, Kohli P (2020) The autoencoding variational autoencoder. Adv Neural Inf Process Syst 33:15077\u201315087","journal-title":"Adv Neural Inf Process Syst"},{"key":"1111_CR6","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"issue":"4","key":"1111_CR7","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.1007\/s10618-022-00885-6","volume":"37","author":"Y Chikahara","year":"2023","unstructured":"Chikahara Y, Sakaue S, Fujino A, Kashima H (2023) Making individually fair predictions with causal pathways. Data Min Knowl Discov 37(4):1327\u20131373","journal-title":"Data Min Knowl Discov"},{"issue":"9","key":"1111_CR8","doi-asserted-by":"publisher","first-page":"10850","DOI":"10.1109\/TPAMI.2023.3261988","volume":"45","author":"F-A Croitoru","year":"2023","unstructured":"Croitoru F-A, Hondru V, Ionescu RT, Shah M (2023) Diffusion models in vision: a survey. IEEE Trans Pattern Anal Mach Intell 45(9):10850\u201310869","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1111_CR9","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal P, Nichol A (2021) Diffusion models beat gans on image synthesis. Adv Neural Inf Process Syst 34:8780\u20138794","journal-title":"Adv Neural Inf Process Syst"},{"key":"1111_CR10","unstructured":"Drummond C, Holte RC, et al (2003) C4.5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling. In: Workshop on learning from imbalanced datasets II, vol 11"},{"key":"1111_CR11","doi-asserted-by":"crossref","unstructured":"Fontanella A, Mair G, Wardlaw J, Trucco E, Storkey A (2024) Diffusion models for counterfactual generation and anomaly detection in brain images. IEEE Trans Med Imaging 2024:1","DOI":"10.1109\/TMI.2024.3460391"},{"issue":"7","key":"1111_CR12","doi-asserted-by":"publisher","first-page":"2288","DOI":"10.1007\/s11263-021-01448-w","volume":"129","author":"M Georgopoulos","year":"2021","unstructured":"Georgopoulos M, Oldfield J, Nicolaou MA, Panagakis Y, Pantic M (2021) Mitigating demographic bias in facial datasets with style-based multi-attribute transfer. Int J Comput Vis 129(7):2288\u20132307","journal-title":"Int J Comput Vis"},{"issue":"1","key":"1111_CR13","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1093\/pan\/mpp036","volume":"18","author":"AN Glynn","year":"2010","unstructured":"Glynn AN, Quinn KM (2010) An introduction to the augmented inverse propensity weighted estimator. Polit Anal 18(1):36\u201356","journal-title":"Polit Anal"},{"key":"1111_CR14","doi-asserted-by":"crossref","unstructured":"Goel N, Amayuelas A, Deshpande A, Sharma A (2021) The importance of modeling data missingness in algorithmic fairness: a causal perspective. In: Proceedings of the AAAI conference on artificial intelligence, pp 7564\u20137573","DOI":"10.1609\/aaai.v35i9.16926"},{"key":"1111_CR15","first-page":"2672","volume":"27","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27:2672\u20132680","journal-title":"Adv Neural Inf Process Syst"},{"issue":"3","key":"1111_CR16","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1007\/s10994-022-06206-8","volume":"112","author":"V Grari","year":"2023","unstructured":"Grari V, Lamprier S, Detyniecki M (2023) Adversarial learning for counterfactual fairness. Mach Learn 112(3):741\u2013763","journal-title":"Mach Learn"},{"issue":"5","key":"1111_CR17","doi-asserted-by":"publisher","first-page":"2770","DOI":"10.1007\/s10618-022-00831-6","volume":"38","author":"R Guidotti","year":"2024","unstructured":"Guidotti R (2024) Counterfactual explanations and how to find them: literature review and benchmarking. Data Min Knowl Discov 38(5):2770\u20132824","journal-title":"Data Min Knowl Discov"},{"key":"1111_CR18","first-page":"3315","volume":"29","author":"M Hardt","year":"2016","unstructured":"Hardt M, Price E, Srebro N (2016) Equality of opportunity in supervised learning. Adv Neural Inf Process Syst 29:3315\u20133323","journal-title":"Adv Neural Inf Process Syst"},{"issue":"11","key":"1111_CR19","doi-asserted-by":"publisher","first-page":"5464","DOI":"10.1109\/TIP.2019.2916751","volume":"28","author":"Z He","year":"2019","unstructured":"He Z, Zuo W, Kan M, Shan S, Chen X (2019) Attgan: facial attribute editing by only changing what you want. IEEE Trans Image Process 28(11):5464\u20135478","journal-title":"IEEE Trans Image Process"},{"key":"1111_CR20","first-page":"6626","volume":"30","author":"M Heusel","year":"2017","unstructured":"Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. Adv Neural Inf Process Syst 30:6626\u20136637","journal-title":"Adv Neural Inf Process Syst"},{"key":"1111_CR21","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho J, Jain A, Abbeel P (2020) Denoising diffusion probabilistic models. Adv Neural Inf Process Syst 33:6840\u20136851","journal-title":"Adv Neural Inf Process Syst"},{"issue":"47","key":"1111_CR22","first-page":"1","volume":"23","author":"J Ho","year":"2022","unstructured":"Ho J, Saharia C, Chan W, Fleet DJ, Norouzi M, Salimans T (2022) Cascaded diffusion models for high fidelity image generation. J Mach Learn Res 23(47):1\u201333","journal-title":"J Mach Learn Res"},{"key":"1111_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106264","volume":"175","author":"Y Hu","year":"2024","unstructured":"Hu Y, Liao T, Chen J, Bian J, Zheng Z, Chen C (2024) Migrate demographic group for fair graph neural networks. Neural Netw 175:106264","journal-title":"Neural Netw"},{"key":"1111_CR24","doi-asserted-by":"crossref","unstructured":"Huberman-Spiegelglas I, Kulikov V, Michaeli T (2024) An edit friendly ddpm noise space: inversion and manipulations. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12469\u201312478","DOI":"10.1109\/CVPR52733.2024.01185"},{"key":"1111_CR25","doi-asserted-by":"crossref","unstructured":"Jang T, Zheng F, Wang X (2021) Constructing a fair classifier with generated fair data. In: Proceedings of the AAAI conference on artificial intelligence, pp 7908\u20137916","DOI":"10.1609\/aaai.v35i9.16965"},{"key":"1111_CR26","doi-asserted-by":"crossref","unstructured":"Jeanneret G, Simon L, Jurie F (2022) Diffusion models for counterfactual explanations. In: Proceedings of the Asian conference on computer vision, pp 858\u2013876","DOI":"10.1007\/978-3-031-26293-7_14"},{"key":"1111_CR27","first-page":"16133","volume":"37","author":"X Ji","year":"2024","unstructured":"Ji X, Xue A, Wong E, Sokolsky O, Lee I (2024) Ar-pro: counterfactual explanations for anomaly repair with formal properties. Adv Neural Inf Process Syst 37:16133\u201316159","journal-title":"Adv Neural Inf Process Syst"},{"key":"1111_CR28","unstructured":"Karras T, Aila T, Laine S, Lehtinen J (2018) Progressive growing of gans for improved quality, stability, and variation. In: Proceedings of the international conference on learning representations"},{"key":"1111_CR29","doi-asserted-by":"crossref","unstructured":"Kehrenberg T, Bartlett M, Thomas O, Quadrianto N (2020) Null-sampling for interpretable and fair representations. In: Proceedings of the European conference on computer vision, pp 565\u2013580","DOI":"10.1007\/978-3-030-58574-7_34"},{"key":"1111_CR30","volume-title":"Auto-encoding variational bayes","author":"DP Kingma","year":"2013","unstructured":"Kingma DP, Welling M et al (2013) Auto-encoding variational bayes, Banff, Canada"},{"key":"1111_CR31","first-page":"728","volume":"33","author":"P Lahoti","year":"2020","unstructured":"Lahoti P, Beutel A, Chen J, Lee K, Prost F, Thain N, Wang X, Chi E (2020) Fairness without demographics through adversarially reweighted learning. Adv Neural Inf Process Syst 33:728\u2013740","journal-title":"Adv Neural Inf Process Syst"},{"key":"1111_CR32","doi-asserted-by":"crossref","unstructured":"Lee J-G, Roh Y, Song H, Whang SE (2021) Machine learning robustness, fairness, and their convergence. In: Proceedings of the ACM SIGKDD conference on knowledge discovery & data mining, pp 4046\u20134047","DOI":"10.1145\/3447548.3470799"},{"key":"1111_CR33","doi-asserted-by":"crossref","unstructured":"Lei J, Tang J, Jia K (2023) Rgbd2: generative scene synthesis via incremental view inpainting using rgbd diffusion models. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8422\u20138434","DOI":"10.1109\/CVPR52729.2023.00814"},{"key":"1111_CR34","doi-asserted-by":"crossref","unstructured":"Liang H, Perona P, Balakrishnan G (2023) Benchmarking algorithmic bias in face recognition: an experimental approach using synthetic faces and human evaluation. In: Proceedings of the IEEE international conference on computer vision, pp 4977\u20134987","DOI":"10.1109\/ICCV51070.2023.00459"},{"key":"1111_CR35","doi-asserted-by":"crossref","unstructured":"Li J, Moskovitch Y, Jagadish H (2021) Denouncer: detection of unfairness in classifiers. Proc VLDB Endow 14(12)","DOI":"10.14778\/3476311.3476328"},{"key":"1111_CR36","doi-asserted-by":"crossref","unstructured":"Liu Z, Luo P, Wang X, Tang X (2015) Deep learning face attributes in the wild. In: Proceedings of the IEEE international conference on computer vision, pp 3730\u20133738","DOI":"10.1109\/ICCV.2015.425"},{"key":"1111_CR37","doi-asserted-by":"crossref","unstructured":"Li Y, Yu Z, He G, Shen Y, Li K, Sun X, Lin S (2024) Spd-ddpm: Denoising diffusion probabilistic models in the symmetric positive definite space. In: Proceedings of the AAAI conference on artificial intelligence, vol 38, pp 13709\u201313717","DOI":"10.1609\/aaai.v38i12.29276"},{"key":"1111_CR38","doi-asserted-by":"crossref","unstructured":"Lohr S (2022) Facial recognition is accurate, if you\u2019re a white guy. In: Ethics of data and analytics, pp 143\u2013147","DOI":"10.1201\/9781003278290-22"},{"key":"1111_CR39","doi-asserted-by":"crossref","unstructured":"Lugmayr A, Danelljan M, Romero A, Yu F, Timofte R, Van\u00a0Gool L (2022) Repaint: inpainting using denoising diffusion probabilistic models. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11461\u201311471","DOI":"10.1109\/CVPR52688.2022.01117"},{"key":"1111_CR40","doi-asserted-by":"crossref","unstructured":"Madhavan R, Wadhwa M (2020) Fairness-aware learning with prejudice free representations. In: Proceedings of the ACM international conference on information & knowledge management, pp 2137\u20132140","DOI":"10.1145\/3340531.3412150"},{"issue":"11","key":"1111_CR41","doi-asserted-by":"publisher","first-page":"2519","DOI":"10.14778\/3476249.3476299","volume":"14","author":"F Nargesian","year":"2021","unstructured":"Nargesian F, Asudeh A, Jagadish H (2021) Tailoring data source distributions for fairness-aware data integration. Proc VLDB Endow 14(11):2519\u20132532","journal-title":"Proc VLDB Endow"},{"key":"1111_CR42","doi-asserted-by":"crossref","unstructured":"Orr L, Balazinska M, Suciu D (2020) Sample debiasing in the themis open world database system. In: Proceedings of the ACM SIGMOD international conference on management of data, pp 257\u2013268","DOI":"10.1145\/3318464.3380606"},{"key":"1111_CR43","doi-asserted-by":"crossref","unstructured":"Pan Y, Froese F, Liu N, Hu Y, Ye M (2023) The adoption of artificial intelligence in employee recruitment: the influence of contextual factors. In: Artificial intelligence and international HRM, pp 60\u201382","DOI":"10.4324\/9781003377085-3"},{"key":"1111_CR44","doi-asserted-by":"crossref","unstructured":"Park S, Hwang S, Kim D, Byun H (2021) Learning disentangled representation for fair facial attribute classification via fairness-aware information alignment. In: Proceedings of the AAAI conference on artificial intelligence, pp 2403\u20132411","DOI":"10.1609\/aaai.v35i3.16341"},{"key":"1111_CR45","doi-asserted-by":"crossref","unstructured":"Park T, Jung S, Chun S, Moon T (2024) Fairdro: group fairness regularization via classwise robust optimization. Neural Netw 106891","DOI":"10.1016\/j.neunet.2024.106891"},{"key":"1111_CR46","doi-asserted-by":"crossref","unstructured":"Pastor E, Bonchi F (2024) Intersectional fair ranking via subgroup divergence. Data Min Knowl Discov 1\u201337","DOI":"10.1007\/s10618-024-01029-8"},{"issue":"3","key":"1111_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3494672","volume":"55","author":"D Pessach","year":"2022","unstructured":"Pessach D, Shmueli E (2022) A review on fairness in machine learning. ACM Comput Surv (CSUR) 55(3):1\u201344","journal-title":"ACM Comput Surv (CSUR)"},{"key":"1111_CR48","doi-asserted-by":"crossref","unstructured":"Raji ID, Gebru T, Mitchell M, Buolamwini J, Lee J, Denton E (2020) Saving face: investigating the ethical concerns of facial recognition auditing. In: Proceedings of the AAAI\/ACM conference on AI, ethics, and society, pp 145\u2013151","DOI":"10.1145\/3375627.3375820"},{"key":"1111_CR49","doi-asserted-by":"crossref","unstructured":"Ramaswamy VV, Kim SS, Russakovsky O (2021) Fair attribute classification through latent space de-biasing. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9301\u20139310","DOI":"10.1109\/CVPR46437.2021.00918"},{"issue":"9","key":"1111_CR50","doi-asserted-by":"publisher","first-page":"1694","DOI":"10.14778\/3461535.3463474","volume":"14","author":"R Salazar","year":"2021","unstructured":"Salazar R, Neutatz F, Abedjan Z (2021) Automated feature engineering for algorithmic fairness. Proc VLDB Endow 14(9):1694\u20131702","journal-title":"Proc VLDB Endow"},{"key":"1111_CR51","first-page":"2226","volume":"29","author":"T Salimans","year":"2016","unstructured":"Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training gans. Adv Neural Inf Process Syst 29:2226\u20132234","journal-title":"Adv Neural Inf Process Syst"},{"key":"1111_CR52","unstructured":"Sharma A, Kiciman E (2020) Dowhy: An end-to-end library for causal inference. Preprint arXiv:2011.04216"},{"key":"1111_CR53","doi-asserted-by":"crossref","unstructured":"Shetiya S, Swift IP, Asudeh A, Das G (2022) Fairness-aware range queries for selecting unbiased data. In: Proceedings of the IEEE international conference on data engineering (ICDE). IEEE, pp 1423\u20131436","DOI":"10.1109\/ICDE53745.2022.00111"},{"key":"1111_CR54","unstructured":"Sohl-Dickstein J, Weiss E, Maheswaranathan N, Ganguli S (2015) Deep unsupervised learning using nonequilibrium thermodynamics. In: International conference on machine learning, pp 2256\u20132265. pmlr"},{"key":"1111_CR55","doi-asserted-by":"crossref","unstructured":"Um S, Suh C (2023) A fair generative model using lecam divergence. In: Proceedings of the AAAI conference on artificial intelligence, pp 10034\u201310042","DOI":"10.1609\/aaai.v37i8.26196"},{"issue":"12","key":"1111_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3677119","volume":"56","author":"S Verma","year":"2024","unstructured":"Verma S, Boonsanong V, Hoang M, Hines K, Dickerson J, Shah C (2024) Counterfactual explanations and algorithmic recourses for machine learning: a review. ACM Comput Surv 56(12):1\u201342","journal-title":"ACM Comput Surv"},{"issue":"4","key":"1111_CR57","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"1111_CR58","doi-asserted-by":"crossref","unstructured":"Wang W, Feng F, He X, Zhang H, Chua T-S (2021) Clicks can be cheating: Counterfactual recommendation for mitigating clickbait issue. In: Proceedings of the international ACM conference on research and development in information retrieval, pp 1288\u20131297","DOI":"10.1145\/3404835.3462962"},{"key":"1111_CR59","doi-asserted-by":"crossref","unstructured":"Wei Y, Shu W, Cheng Z, Tai W, Xiao C, Zhong T (2024) Counterfactual graph learning for anomaly detection with feature disentanglement and generation (student abstract). In: Proceedings of the AAAI conference on artificial intelligence, vol 38, pp 23682\u201323683","DOI":"10.1609\/aaai.v38i21.30524"},{"key":"1111_CR60","doi-asserted-by":"crossref","unstructured":"Wen Y, Ma X, Zhang X, Pun M-O (2024) Gcd-ddpm: a generative change detection model based on difference-feature guided ddpm. IEEE Trans Geosci Remote Sens 2024:1","DOI":"10.1109\/TGRS.2024.3381752"},{"key":"1111_CR61","doi-asserted-by":"crossref","unstructured":"Xu X, Huang Y, Shen P, Li S, Li J, Huang F, Li Y, Cui Z (2021) Consistent instance false positive improves fairness in face recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 578\u2013586","DOI":"10.1109\/CVPR46437.2021.00064"},{"issue":"4","key":"1111_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3626235","volume":"56","author":"L Yang","year":"2023","unstructured":"Yang L, Zhang Z, Song Y, Hong S, Xu R, Zhao Y, Zhang W, Cui B, Yang M-H (2023) Diffusion models: a comprehensive survey of methods and applications. ACM Comput Surv 56(4):1\u201339","journal-title":"ACM Comput Surv"},{"key":"1111_CR63","doi-asserted-by":"crossref","unstructured":"Yang Y, Gupta A, Feng J, Singhal P, Yadav V, Wu Y, Natarajan P, Hedau V, Joo J (2022) Enhancing fairness in face detection in computer vision systems by demographic bias mitigation. In: Proceedings of the AAAI\/ACM conference on AI, ethics, and society, pp 813\u2013822","DOI":"10.1145\/3514094.3534153"},{"key":"1111_CR64","doi-asserted-by":"crossref","unstructured":"Yu J, Hao X, Cui Z, He P, Liu T (2021) Boosting fairness for masked face recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 1531\u20131540","DOI":"10.1109\/ICCVW54120.2021.00178"},{"key":"1111_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106781","volume":"181","author":"G Zhang","year":"2025","unstructured":"Zhang G, Yuan G, Cheng D, Liu L, Li J, Zhang S (2025) Disentangled contrastive learning for fair graph representations. Neural Netw 181:106781","journal-title":"Neural Netw"},{"key":"1111_CR66","doi-asserted-by":"crossref","unstructured":"Zhang R, Isola P, Efros AA, Shechtman E, Wang O (2018) The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 586\u2013595","DOI":"10.1109\/CVPR.2018.00068"},{"key":"1111_CR67","doi-asserted-by":"crossref","unstructured":"Zhao Z, Bai H, Zhu Y, Zhang J, Xu S, Zhang Y, Zhang K, Meng D, Timofte R, Van\u00a0Gool L (2023) DDFM: denoising diffusion model for multi-modality image fusion. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 8082\u20138093","DOI":"10.1109\/ICCV51070.2023.00742"},{"key":"1111_CR68","doi-asserted-by":"crossref","unstructured":"Zhao X, Fan W, Liu H, Tang J (2022) Multi-type urban crime prediction. In: Proceedings of the AAAI conference on artificial intelligence, pp 4388\u20134396","DOI":"10.1609\/aaai.v36i4.20360"},{"key":"1111_CR69","doi-asserted-by":"crossref","unstructured":"Zhao Y, Wang Y, Derr T (2023) Fairness and explainability: bridging the gap towards fair model explanations. In: Proceedings of the AAAI conference on artificial intelligence, pp 11363\u201311371","DOI":"10.1609\/aaai.v37i9.26344"},{"issue":"3","key":"1111_CR70","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1109\/TCBB.2020.2994780","volume":"18","author":"X Zhou","year":"2020","unstructured":"Zhou X, Li Y, Liang W (2020) Cnn-rnn based intelligent recommendation for online medical pre-diagnosis support. IEEE\/ACM Trans Comput Biol Bioinf 18(3):912\u2013921","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-025-01111-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-025-01111-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-025-01111-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T15:50:00Z","timestamp":1757173800000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-025-01111-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,27]]},"references-count":70,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["1111"],"URL":"https:\/\/doi.org\/10.1007\/s10618-025-01111-9","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,27]]},"assertion":[{"value":"9 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"34"}}