Fetching the paper…
Reading the bibliography…
Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc.
1901
Earlier work this paper cites.
Shortliffe EH, Davis R, Axline SG, Buchanan BG, Green CC, Cohen SN. Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Comput Biomed Res. 1975;8(4):303–320
1975
Earlier work this paper cites.
Miller R. INTERNIST-1/CADUCEUS: problems facing expert consultant programs. Methods Inf Med. 1984;23(1):9–14
1984
Earlier work this paper cites.
Kherbache A, Mertens E, Denier Y. Moral distress in medicine: an ethical analysis. J Health Psychol. 2022;27(8):1971–1990
1990
Earlier work this paper cites.
Friedman B, Nissenbaum H. Bias in computer systems. ACM Trans Inf Syst. 1996;14(3):330–347. doi: https://doi.org/10.1145/230538.230561
1996
Earlier work this paper cites.
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–357
2002
Earlier work this paper cites.
2007
Earlier work this paper cites.
van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(86):2579–2605. Available from: http://jmlr.org/papers/v9/vandermaaten08a.html
2008
Earlier work this paper cites.
Fuster V, Kelly BB, Vedanthan R. Global cardiovascular health: urgent need for an intersectoral approach. J Am Coll Cardiol. 2011;58(12):1208–1210. doi:10.1016/j.jacc.2011.05.038
2011
Earlier work this paper cites.
Feldman B, Martin EM, Skotnes T. Big data in healthcare: hype and hope. Dr. Bonnie 360. 2012
2012
Earlier work this paper cites.
Wang S, Summers RM. Machine learning and radiology. Med Image Anal. 2012;16(5):933–951
2012
Earlier work this paper cites.
Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R. Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference. New York: ACM; 2012. p. 214–226
2012
Earlier work this paper cites.
Kamiran F, Calders T. Data preprocessing techniques for classification without discrimination. Knowl Inf Syst. 2012;33(1):1–33
2012
Earlier work this paper cites.
Ramos-Murguialday A, Broetz D, Rea M, Läer L, Yilmaz Ö, Brasil FL, et al. Brain–machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol. 2013;74(1):100–108
2013
Earlier work this paper cites.
Zemel R, Wu Y, Swersky K, Pitassi T, Dwork C. Learning fair representations. In: Proceedings of the 30th International Conference on Machine Learning. PMLR; 2013. p. 325–333
2013
Earlier work this paper cites.
Fenton JJ, Xing G, Elmore JG, Bang H, Chen SL, Lindfors KK, et al. Short-term outcomes of screening mammography using computer-aided detection: a population-based study of Medicare enrollees. Ann Intern Med. 2013;158(8):580–587
2013
Earlier work this paper cites.
Ambale-Venkatesh B, Lima JA. Cardiac MRI: a central prognostic tool in myocardial fibrosis. Nat Rev Cardiol. 2015;12(1):18–29. doi:10.1038/nrcardio.2014.159
2014
Earlier work this paper cites.
Larson HJ, Jarrett C, Eckersberger E, Smith DM, Paterson P. Understanding vaccine hesitancy around vaccines and vaccination from a global perspective: a systematic review of published literature, 2007–2012. Vaccine. 2014;32(19):2150–2159
2014
Earlier work this paper cites.
Luxton DD. Artificial intelligence in psychological practice: current and future applications and implications. Prof Psychol Res Pr. 2014;45(5):332–339
2014
Earlier work this paper cites.
Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8–17
2015
Earlier work this paper cites.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–444
2015
Earlier work this paper cites.
Semigran HL, Linder JA, Gidengil C, Mehrotra A. Evaluation of symptom checkers for self-diagnosis and triage: audit study. BMJ. 2015;351:h3480
2015
Earlier work this paper cites.
Henry C. Hospital closures: the sociospatial restructuring of labor and health care. Ann Assoc Am Geogr. 2015;105(5):1094–1110
2015
Earlier work this paper cites.
Jha S, Topol EJ. Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA. 2016;316(22):2353–2354
2016
Earlier work this paper cites.
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–2410. doi:10.1001/jama.2016.17216
2016
Earlier work this paper cites.
Fornito A, Zalesky A, Bullmore E. Fundamentals of brain network analysis. London: Academic Press; 2016. doi:10.1016/C2012-0-06036-X
2016
Earlier work this paper cites.
Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L. The ethics of algorithms: mapping the debate. Big Data Soc. 2016;3(2):2053951716679679
2016
Earlier work this paper cites.
Hardt M, Price E, Srebro N. Equality of opportunity in supervised learning. Adv Neural Inf Process Syst. 2016;29
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Ribeiro MT, Singh S, Guestrin C. ”Why should I trust you?”: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM; 2016. p. 1135–1144
2016
Earlier work this paper cites.
Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216-1219
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230–243
2017
Earlier work this paper cites.
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88
2017
Earlier work this paper cites.
Voigt P, von dem Bussche A. The EU General Data Protection Regulation (GDPR): A Practical Guide. 1st ed. Cham: Springer International Publishing; 2017. doi:10.1007/978-3-319-57959-7
2017
Earlier work this paper cites.
Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69(21):2657–2664. doi:10.1016/j.jacc.2017.03.571
2017
Earlier work this paper cites.
Ting DSW, Cheung CYL, Lim G, Tan GSW, Quang ND, Gan A, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA. 2017;318(22):2211–2223
2017
Earlier work this paper cites.
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118
2017
Earlier work this paper cites.
Kerasidou A. Trust me, I’m a researcher!: the role of trust in biomedical research. Med Health Care Philos. 2017;20(1):43–50
2017
Earlier work this paper cites.
Kusner MJ, Loftus JR, Russell C, Silva R. Counterfactual fairness. Adv Neural Inf Process Syst. 2017;30:4066–4076
2017
Earlier work this paper cites.
Kamiran F, Mansha S, Karim A, Zhang X. Exploiting reject option in classification for social discrimination control. Inf Sci. 2018;425:18–33. doi:10.1016/j.ins.2017.10.049
2017
Earlier work this paper cites.
Calmon F, Wei D, Vinzamuri B, Natesan Ramamurthy K, Varshney KR. Optimized pre-processing for discrimination prevention. Adv Neural Inf Process Syst. 2017;30
2017
Earlier work this paper cites.
Zafar MB, Valera I, Rodriguez MG, Gummadi KP. Fairness constraints: mechanisms for fair classification. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR; 2017. p. 962–970
2017
Earlier work this paper cites.
Pleiss G, Raghavan M, Wu F, Kleinberg J, Weinberger KQ. On fairness and calibration. Adv Neural Inf Process Syst. 2017;30
2017
Earlier work this paper cites.
Lubarsky B. Re-identification of “anonymized” data. Geo L Tech Rev. 2017;1:202
2017
Earlier work this paper cites.
Terry NP. Regulatory disruption and arbitrage in health-care data protection. Yale J Health Policy Law Ethics. 2017;17:143-208
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;30
2017
Earlier work this paper cites.
Goodman B, Flaxman S. European Union regulations on algorithmic decision-making and a “right to explanation”. AI Mag. 2017;38(3):50–57
2017
Earlier work this paper cites.
Adamson AS, Smith A. Machine learning and health care disparities in dermatology. JAMA Dermatol. 2018;154(11):1247–1248. doi:10.1001/jamadermatol.2018.2348
2018
Earlier work this paper cites.
Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018;71(23):2668–2679. doi:10.1016/j.jacc.2018.03.521
2018
Earlier work this paper cites.
Madani A, Arnaout R, Mofrad M, Arnaout R. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med. 2018;1(1):6. doi:10.1038/s41746-017-0013-1
2018
Earlier work this paper cites.
De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24(9):1342–1350. doi:10.1038/s41591-018-0107-6
2018
Earlier work this paper cites.
Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172(5):1122–1131
2018
Earlier work this paper cites.
Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1(1):39
2018
Earlier work this paper cites.
Han SS, Kim MS, Lim W, Park GH, Park I, Chang SE. Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J Invest Dermatol. 2018;138(7):1529–1538
2018
Earlier work this paper cites.
Dorsey ER, Bloem BR. The Parkinson pandemic—a call to action. JAMA Neurol. 2018;75(1):9–10
2018
Earlier work this paper cites.
Bzdok D, Meyer-Lindenberg A. Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3(3):223–230
2018
Earlier work this paper cites.
Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJ. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500–510
2018
Cited alongside, same era.
Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719–731
2018
Cited alongside, same era.
Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: addressing ethical challenges. PLoS Med. 2018;15(11):e1002689
2018
Cited alongside, same era.
Veinot TC, Mitchell H, Ancker JS. Good intentions are not enough: how informatics interventions can worsen inequality. J Am Med Inform Assoc. 2018;25(8):1080–1088
2018
Cited alongside, same era.
Char DS, Shah NH, Magnus D. Implementing machine learning in health care—addressing ethical challenges. N Engl J Med. 2018;378(11):981-983
2018
Cited alongside, same era.
2022
Later among the works it cites.
Lee YTH, Di M, Schrager JD, Buckareff Z, Patzer RE, Yaffee AQ. The use of a self-triage tool to predict COVID-19 cases and hospitalizations in the state of Georgia. West J Emerg Med. 2022;23(4):532-535
2022
Later among the works it cites.
Pessach D, Shmueli E. A review on fairness in machine learning. ACM Comput Surv. 2022;55(3):1–44
2022
Later among the works it cites.
Gottlieb ER, Ziegler J, Morley K, Rush B, Celi LA. Assessment of racial and ethnic differences in oxygen supplementation among patients in the intensive care unit. JAMA Intern Med. 2022;182(8):849–858. doi:10.1001/jamainternmed.2022.2587
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zhang BH, Lemoine B, Mitchell M. Mitigating unwanted biases with adversarial learning. In: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. 2018. p. 335–340
2018
Cited alongside, same era.
Donini M, Oneto L, Ben-David S, Shawe-Taylor JS, Pontil M. Empirical risk minimization under fairness constraints. Adv Neural Inf Process Syst. 2018;31
2018
Cited alongside, same era.
Siontis KC, Zhang X, Eckard A, Bhave N, Schaubel DE, He K, et al. Outcomes associated with apixaban use in patients with end-stage kidney disease and atrial fibrillation in the United States. Circulation. 2018;138(15):1519–1529
2018
Cited alongside, same era.
Hall MA, Orentlicher D, Bobinski MA, Bagley N, Cohen IG. Medical liability and treatment relationships. Aspen Publishing; 2018
2018
Cited alongside, same era.
Chen IY, Johansson FD, Sontag D. Why is my classifier discriminatory? Adv Neural Inf Process Syst. 2018;31
2018
Cited alongside, same era.
Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317–1318
2018
Cited alongside, same era.
Zhang W, Wang J. Content-bootstrapped collaborative filtering for medical article recommendations. IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2018
2018
Cited alongside, same era.
Le Quy T, Roy A, Iosifidis V, Zhang W, Ntoutsi E. A survey on datasets for fairness-aware machine learning. WIREs Data Min Knowl Discov. 2022;12(3):e1452
2022
Later among the works it cites.
Grari V, Lamprier S, Detyniecki M. Fairness without the sensitive attribute via causal variational autoencoder. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-2022). International Joint Conferences on Artificial Intelligence Organization; 2022. p. 696–702. doi:10.24963/ijcai.2022/98
2022
Later among the works it cites.
Zhang W, Weiss JC. Longitudinal fairness with censorship. Proceedings of the AAAI Conference on Artificial Intelligence. 2022;36(11):12235-12243
2022
Later among the works it cites.
Pagano TP, Loureiro RB, Lisboa FVN, Peixoto RM, Guimarães GAS, Cruz GOR, et al. Bias and unfairness in machine learning models: a systematic review on datasets, tools, fairness metrics, and identification and mitigation methods. Big Data Cogn Comput. 2023;7(1):15
2023
Later among the works it cites.
Mittermaier M, Raza MM, Kvedar JC. Bias in AI-based models for medical applications: challenges and mitigation strategies. NPJ Digit Med. 2023;6(1):113
2023
Later among the works it cites.
Corbett-Davies S, Gaebler JD, Nilforoshan H, Shroff R, Goel S. The measure and mismeasure of fairness. J Mach Learn Res. 2023;24(1):14730–14846
2023
Later among the works it cites.
Zhang W, Weiss JC. Fair decision-making under uncertainty. In: Proceedings of the IEEE International Conference on Data Mining (ICDM); 2023. p. 886–895. doi:10.48550/arXiv.2301.12364
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Saxena NA, Zhang W, Shahabi C. Missed Opportunities in Fair AI. Proceedings of the 2023 SIAM International Conference on Data Mining (SDM). 2023:961-964
2023
Later among the works it cites.
Zhang W, Hernandez-Boussard T, Weiss J. Censored fairness through awareness. Proceedings of the AAAI conference on artificial intelligence. 2023;37(12):14611-14619
2023
Later among the works it cites.
Zhang W, Weiss JC. Fairness with censorship and group constraints. Knowledge and Information Systems. 2023:1-24
2023
Later among the works it cites.
Wang Z, Saxena N, Yu T, Karki S, Zetty T, Haque I, Zhou S, Kc D, Stockwell I, Bifet A, et al. Preventing Discriminatory Decision-making in Evolving Data Streams. Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT). 2023
2023
Later among the works it cites.
Zhang W, Wang Z, Kim J, Cheng C, Oommen T, Ravikumar P, Weiss J. Individual Fairness under Uncertainty. 26th European Conference on Artificial Intelligence. 2023:3042-3049
2023
Later among the works it cites.
Wang Z, Wallace C, Bifet A, Yao X, Zhang W. FG 2 AN: Fairness-Aware Graph Generative Adversarial Networks. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2023:259-275
2023
Later among the works it cites.
Wang Z, Narasimhan G, Yao X, Zhang W. Mitigating multisource biases in graph neural networks via real counterfactual samples. 2023 IEEE International Conference on Data Mining (ICDM). 2023:638-647
2023
Later among the works it cites.
Chinta SV, Fernandes K, Cheng N, Fernandez J, Yazdani S, Yin Z, Wang Z, Wang X, Xu W, Liu J, et al. Optimization and improvement of fake news detection using voting technique for societal benefit. 2023 IEEE International Conference on Data Mining Workshops (ICDMW). 2023:1565-1574
2023
Later among the works it cites.
Brann F, Sterling NW, Frisch SO, Schrager JD. Sepsis prediction at emergency department triage using natural language processing: retrospective cohort study. JMIR AI. 2024;3(1):e49784
2024
Closest in time.
Caton S, Haas C. Fairness in Machine Learning: A Survey. ACM Comput Surv. 2024 Jul;56(7):Article 166. doi:10.1145/3616865
2024
Closest in time.
Hort M, Chen Z, Zhang JM, Harman M, Sarro F. Bias mitigation for machine learning classifiers: A comprehensive survey. ACM J Respons Comput. 2024;1(2):1–52. doi:10.1145/3631326
2024
Closest in time.
Ferrara E. Fairness and bias in artificial intelligence: a brief survey of sources, impacts, and mitigation strategies. Sci. 2024;6(1):3. doi:10.3390/sci6010003
2024
Closest in time.
Ueda D, Kakinuma T, Fujita S, Kamagata K, Fushimi Y, Ito R, et al. Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol. 2024;42:3–15. Available from: https://doi.org/10.1007/s11604-023-01474-3
2024
Closest in time.
Cross JL, Choma MA, Onofrey JA. Bias in medical AI: implications for clinical decision-making. PLOS Digit Health. 2024 Nov 7;3(11):e0000651. doi:10.1371/journal.pdig.0000651
2024
Closest in time.
Hardt M, Cila N, Desmet P. Love: the forgotten dimension for just and democratic AI futures. In: Gray C, Ciliotta Chehade E, Hekkert P, Forlano L, Ciuccarelli P, Lloyd P, editors. DRS2024: Boston; 2024 Jun 23–28; Boston, USA. doi:10.21606/drs.2024.909
2024
Closest in time.
Howell MD, Corrado GS, DeSalvo KB. Three epochs of artificial intelligence in health care. JAMA. 2024;331(3):242–244
2024
Closest in time.
Wang Z, Ulloa D, Yu T, Rangaswami R, Yap R, Zhang W. Individual fairness with group constraints in graph neural networks. In: Proceedings of the Conference on Advances in Artificial Intelligence. 2024 October. doi:10.3233/FAIA240679
2024
Closest in time.
Hui W, Lau WK. Detecting and mitigating algorithmic bias in binary classification using causal modeling. In: 2024 4th International Conference on Computer Communication and Information Systems (CCCIS). IEEE; 2024. p. 47–51
2024
Closest in time.
2024
Closest in time.
Zhang W. AI Fairness in Practice: Paradigm, Challenges, and Prospects. Ai Magazine. 2024
2024
Closest in time.
Zhang W. Fairness with Censorship: Bridging the Gap between Fairness Research and Real-World Deployment. Proceedings of the AAAI Conference on Artificial Intelligence. 2024;38(20):22685-22685
2024
Closest in time.
Yazdani S, Saxena N, Wang Z, Wu Y, Zhang W. A Comprehensive Survey of Image and Video Generative AI: Recent Advances, Variants, and Applications. 2024
2024
Closest in time.
Chu Z, Wang Z, Zhang W. Fairness in Large Language Models: A Taxonomic Survey. ACM SIGKDD Explorations Newsletter, 2024. 2024:34-48
2024
Closest in time.
2024
Closest in time.
Yin Z, Wang Z, Zhang W. Improving Fairness in Machine Learning Software via Counterfactual Fairness Thinking. Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings. 2024:420-421
2024
Closest in time.
Wang Z, Qiu M, Chen M, Salem MB, Yao X, Zhang W. Toward Fair Graph Neural Networks via Real Counterfactual Samples. Knowledge and Information Systems. 2024:1-25
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Wang Z, Dzuong J, Yuan X, Chen Z, Wu Y, Yao X, Zhang W. Individual Fairness with Group Awareness Under Uncertainty. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2024:89-106
2024
Closest in time.
Doan TV, Wang Z, Hoang NNM, Zhang W. Fairness in large language models in three hours. Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. 2024:5514-5517
2024
Closest in time.
Wang Z, Chu Z, Blanco R, Chen Z, Chen SC, Zhang W. Advancing Graph Counterfactual Fairness through Fair Representation Learning. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2024:40-58
2024
Closest in time.
Wang Z, Zhang W. Group Fairness with Individual and Censorship Constraints. 27th European Conference on Artificial Intelligence. 2024
2024
Closest in time.
Wang Z, Ulloa D, Yu T, Rangaswami R, Yap R, Zhang W. Individual Fairness with Group Constraints in Graph Neural Networks. 27th European Conference on Artificial Intelligence. 2024
2024
Closest in time.
Yin Z, Agarwal S, Kashif A, Gonzalez M, Wang Z, Liu S, Liu Z, Wu Y, Stockwell I, Xu W, et al. Accessible Health Screening Using Body Fat Estimation by Image Segmentation. 2024 IEEE International Conference on Data Mining Workshops (ICDMW). 2024:405-414
2024
Closest in time.
Zhang W, Zhou S, Walsh T, Weiss JC. Fairness amidst non-IID graph data: A literature review. AI Magazine. 2025;46(1):e12212
2025
Closest in time.
Wang Z, Yin Z, Zhang Y, Yang L, Zhang T, Pissinou N, Cai Y, Hu S, Li Y, Zhao L, et al. FG-SMOTE: Towards Fair Node Classification with Graph Neural Network. ACM SIGKDD Explorations Newsletter. 2025;26(2):99-108
2025
Closest in time.
Wang Z, Yin Z, Liu F, Liu Z, Lisetti C, Yu R, Wang S, Liu J, Ganapati S, Zhou S, et al. Graph Fairness via Authentic Counterfactuals: Tackling Structural and Causal Challenges. ACM SIGKDD Explorations Newsletter. 2025;26(2):89-98
2025
Closest in time.
Wang Z, Chu Z, Viet Doan T, Wang S, Wu Y, Palade V, Zhang W. Fair Graph U-Net: A Fair Graph Learning Framework Integrating Group and Individual Awareness. proceedings of the AAAI conference on artificial intelligence. 2025;39(27):28485-28493
2025
Closest in time.
Wang Z, Hoang N, Zhang X, Bello K, Zhang X, Iyengar SS, Zhang W. Towards Fair Graph Learning without Demographic Information. The 28th International Conference on Artificial Intelligence and Statistics. 2025
2025
Closest in time.
2025
Closest in time.
Wang Z, Zhang W. FDGen: A Fairness-Aware Graph Generation Model. Proceedings of the 42nd International Conference on Machine Learning. 2025
2025
Closest in time.
Wang Z, Liu F, Pan S, Liu J, Saeed F, Qiu M, Zhang W. fairGNN-WOD: Fair Graph Learning Without Complete Demographics. Proceedings of the 34th International Joint Conference on Artificial Intelligence. 2025
2025
Closest in time.
Wang Z, Wu A, Moniz N, Hu S, Knijnenburg B, Zhu Q, Zhang W. Towards Fairness with Limited Demographics via Disentangled Learning. Proceedings of the 34th International Joint Conference on Artificial Intelligence. 2025
2025
Closest in time.
Krittanawong C, Johnson KW, Rosenson RS, Wang Z, Aydar M, Baber U, et al. Deep learning for cardiovascular medicine: a practical primer. Eur Heart J. 2019;40(25):2058–2073. doi:10.1093/eurheartj/ehz056
2073
Closest in time.