Статті в журналах з теми "Fairness-Accuracy trade-Off"
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Jang, Taeuk, Pengyi Shi, and Xiaoqian Wang. "Group-Aware Threshold Adaptation for Fair Classification." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6988–95. http://dx.doi.org/10.1609/aaai.v36i6.20657.
Langenberg, Anna, Shih-Chi Ma, Tatiana Ermakova, and Benjamin Fabian. "Formal Group Fairness and Accuracy in Automated Decision Making." Mathematics 11, no. 8 (April 7, 2023): 1771. http://dx.doi.org/10.3390/math11081771.
Tae, Ki Hyun, Hantian Zhang, Jaeyoung Park, Kexin Rong, and Steven Euijong Whang. "Falcon: Fair Active Learning Using Multi-Armed Bandits." Proceedings of the VLDB Endowment 17, no. 5 (January 2024): 952–65. http://dx.doi.org/10.14778/3641204.3641207.
Badar, Maryam, Sandipan Sikdar, Wolfgang Nejdl, and Marco Fisichella. "FairTrade: Achieving Pareto-Optimal Trade-Offs between Balanced Accuracy and Fairness in Federated Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (March 24, 2024): 10962–70. http://dx.doi.org/10.1609/aaai.v38i10.28971.
Li, Xuran, Peng Wu, and Jing Su. "Accurate Fairness: Improving Individual Fairness without Trading Accuracy." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (June 26, 2023): 14312–20. http://dx.doi.org/10.1609/aaai.v37i12.26674.
Silvia, Chiappa, Jiang Ray, Stepleton Tom, Pacchiano Aldo, Jiang Heinrich, and Aslanides John. "A General Approach to Fairness with Optimal Transport." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3633–40. http://dx.doi.org/10.1609/aaai.v34i04.5771.
Pinzón, Carlos, Catuscia Palamidessi, Pablo Piantanida, and Frank Valencia. "On the Impossibility of Non-trivial Accuracy in Presence of Fairness Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7993–8000. http://dx.doi.org/10.1609/aaai.v36i7.20770.
Singh, Arashdeep, Jashandeep Singh, Ariba Khan, and Amar Gupta. "Developing a Novel Fair-Loan Classifier through a Multi-Sensitive Debiasing Pipeline: DualFair." Machine Learning and Knowledge Extraction 4, no. 1 (March 12, 2022): 240–53. http://dx.doi.org/10.3390/make4010011.
Gitiaux, Xavier, and Huzefa Rangwala. "Fair Representations by Compression." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 11506–15. http://dx.doi.org/10.1609/aaai.v35i13.17370.
Gao, Shiqi, Xianxian Li, Zhenkui Shi, Peng Liu, and Chunpei Li. "Towards Fair and Decentralized Federated Learning System for Gradient Boosting Decision Trees." Security and Communication Networks 2022 (August 2, 2022): 1–18. http://dx.doi.org/10.1155/2022/4202084.
Pan, Chenglu, Jiarong Xu, Yue Yu, Ziqi Yang, Qingbiao Wu, Chunping Wang, Lei Chen, and Yang Yang. "Towards Fair Graph Federated Learning via Incentive Mechanisms." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (March 24, 2024): 14499–507. http://dx.doi.org/10.1609/aaai.v38i13.29365.
Li, Yanying, Xiuling Wang, Yue Ning, and Hui Wang. "FairLP: Towards Fair Link Prediction on Social Network Graphs." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 628–39. http://dx.doi.org/10.1609/icwsm.v16i1.19321.
Zeng, Ziqian, Rashidul Islam, Kamrun Naher Keya, James Foulds, Yangqiu Song, and Shimei Pan. "Fair Representation Learning for Heterogeneous Information Networks." Proceedings of the International AAAI Conference on Web and Social Media 15 (May 22, 2021): 877–87. http://dx.doi.org/10.1609/icwsm.v15i1.18111.
Sun, Ying, Fariborz Haghighat, and Benjamin C. M. Fung. "Trade-off between accuracy and fairness of data-driven building and indoor environment models: A comparative study of pre-processing methods." Energy 239 (January 2022): 122273. http://dx.doi.org/10.1016/j.energy.2021.122273.
Sun, Ying, Fariborz Haghighat, and Benjamin C. M. Fung. "Trade-off between accuracy and fairness of data-driven building and indoor environment models: A comparative study of pre-processing methods." Energy 239 (January 2022): 122273. http://dx.doi.org/10.1016/j.energy.2021.122273.
Li, Qin, Zhou, Cheng, Zhang, and Ai. "Intelligent Rapid Adaptive Offloading Algorithm for Computational Services in Dynamic Internet of Things System." Sensors 19, no. 15 (August 4, 2019): 3423. http://dx.doi.org/10.3390/s19153423.
HERBORDT, MARTIN C., and CHARLES C. WEEMS. "ENPASSANT: AN ENVIRONMENT FOR EVALUATING MASSIVELY PARALLEL ARRAY ARCHITECTURES FOR SPATIALLY MAPPED APPLICATIONS." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 02 (April 1995): 175–200. http://dx.doi.org/10.1142/s0218001495000109.
Kieslich, Kimon, Birte Keller, and Christopher Starke. "Artificial intelligence ethics by design. Evaluating public perception on the importance of ethical design principles of artificial intelligence." Big Data & Society 9, no. 1 (January 2022): 205395172210929. http://dx.doi.org/10.1177/20539517221092956.
Costa, Diogo, Miguel Costa, and Sandro Pinto. "Train Me If You Can: Decentralized Learning on the Deep Edge." Applied Sciences 12, no. 9 (May 6, 2022): 4653. http://dx.doi.org/10.3390/app12094653.
Schwartz, Jessica M., Maureen George, Sarah Collins Rossetti, Patricia C. Dykes, Simon R. Minshall, Eugene Lucas, and Kenrick D. Cato. "Factors Influencing Clinician Trust in Predictive Clinical Decision Support Systems for In-Hospital Deterioration: Qualitative Descriptive Study." JMIR Human Factors 9, no. 2 (May 12, 2022): e33960. http://dx.doi.org/10.2196/33960.
Li, Jingyang, and Guoqiang Li. "The Triangular Trade-off between Robustness, Accuracy and Fairness in Deep Neural Networks: A Survey." ACM Computing Surveys, February 12, 2024. http://dx.doi.org/10.1145/3645088.
Talbert, Douglas A., Katherine L. Phillips, Katherine E. Brown, and Steve Talbert. "Assessing and Addressing Model Trustworthiness Trade-offs in Trauma Triage." International Journal on Artificial Intelligence Tools 33, no. 03 (April 25, 2024). http://dx.doi.org/10.1142/s0218213024600078.
Chen, Zhenpeng, Jie M. Zhang, Federica Sarro, and Mark Harman. "A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers." ACM Transactions on Software Engineering and Methodology, February 9, 2023. http://dx.doi.org/10.1145/3583561.
Buijsman, Stefan. "Navigating fairness measures and trade-offs." AI and Ethics, July 17, 2023. http://dx.doi.org/10.1007/s43681-023-00318-0.
Wei, Chen, Kui Xu, Zhexian Shen, Xiaochen Xia, Wei Xie, and Chunguo Li. "Location-aided uplink transmission for user-centric cell-free massive MIMO systems: a fairness priority perspective." EURASIP Journal on Wireless Communications and Networking 2022, no. 1 (September 11, 2022). http://dx.doi.org/10.1186/s13638-022-02171-x.
Duricic, Tomislav, Dominik Kowald, Emanuel Lacic, and Elisabeth Lex. "Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks." Frontiers in Big Data 6 (December 19, 2023). http://dx.doi.org/10.3389/fdata.2023.1251072.
Rueda, Jon, Janet Delgado Rodríguez, Iris Parra Jounou, Joaquín Hortal-Carmona, Txetxu Ausín, and David Rodríguez-Arias. "“Just” accuracy? Procedural fairness demands explainability in AI-based medical resource allocations." AI & SOCIETY, December 21, 2022. http://dx.doi.org/10.1007/s00146-022-01614-9.
Oh, Hyeji, and Chulyun Kim. "Fairness-aware recommendation with meta learning." Scientific Reports 14, no. 1 (May 2, 2024). http://dx.doi.org/10.1038/s41598-024-60808-x.
Zhang, Tao, Tianqing Zhu, Mengde Han, Fengwen Chen, Jing Li, Wanlei Zhou, and Philip S. Yu. "Fairness in graph-based semi-supervised learning." Knowledge and Information Systems, October 1, 2022. http://dx.doi.org/10.1007/s10115-022-01738-w.
Loi, Michele, and Markus Christen. "Choosing how to discriminate: navigating ethical trade-offs in fair algorithmic design for the insurance sector." Philosophy & Technology, March 13, 2021. http://dx.doi.org/10.1007/s13347-021-00444-9.
Scher, Sebastian, Simone Kopeinik, Andreas Trügler, and Dominik Kowald. "Modelling the long-term fairness dynamics of data-driven targeted help on job seekers." Scientific Reports 13, no. 1 (January 31, 2023). http://dx.doi.org/10.1038/s41598-023-28874-9.
Müllner, Peter, Elisabeth Lex, Markus Schedl, and Dominik Kowald. "Differential privacy in collaborative filtering recommender systems: a review." Frontiers in Big Data 6 (October 12, 2023). http://dx.doi.org/10.3389/fdata.2023.1249997.
Islam, Sheikh Rabiul, Ingrid Russell, William Eberle, Douglas Talbert, and Md Golam Moula Mehedi Hasan. "Advances in Explainable, Fair, and Trustworthy AI." International Journal on Artificial Intelligence Tools 33, no. 03 (April 22, 2024). http://dx.doi.org/10.1142/s0218213024030015.
Pal, Manjish, Subham Pokhriyal, Sandipan Sikdar, and Niloy Ganguly. "Ensuring generalized fairness in batch classification." Scientific Reports 13, no. 1 (November 2, 2023). http://dx.doi.org/10.1038/s41598-023-45943-1.
Shanklin, Robert, Michele Samorani, Shannon Harris, and Michael A. Santoro. "Ethical Redress of Racial Inequities in AI: Lessons from Decoupling Machine Learning from Optimization in Medical Appointment Scheduling." Philosophy & Technology 35, no. 4 (October 20, 2022). http://dx.doi.org/10.1007/s13347-022-00590-8.
Szepannek, Gero, and Karsten Lübke. "Facing the Challenges of Developing Fair Risk Scoring Models." Frontiers in Artificial Intelligence 4 (October 14, 2021). http://dx.doi.org/10.3389/frai.2021.681915.
Cortés-Andrés, Jordi, Gustau Camps-Valls, Sebastian Sippel, Eniko Melinda Székely, Dino Sejdinovic, Emiliano Díaz, Adrián Pérez-Suay, Zhu Li, Miguel D. Mahecha, and Markus Reichstein. "Physics-aware nonparametric regression models for Earth data analysis." Environmental Research Letters, April 14, 2022. http://dx.doi.org/10.1088/1748-9326/ac6762.
Pham, Diem, Binh Tran, Su Nguyen, Damminda Alahakoon, and Mengjie Zhang. "Fairness optimisation with multi-objective swarms for explainable classifiers on data streams." Complex & Intelligent Systems, April 3, 2024. http://dx.doi.org/10.1007/s40747-024-01347-w.