Artigos de revistas sobre o tema "Fair Machine Learning"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Veja os 50 melhores artigos de revistas para estudos sobre o assunto "Fair Machine Learning".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Veja os artigos de revistas das mais diversas áreas científicas e compile uma bibliografia correta.
Basu Roy Chowdhury, Somnath, e Snigdha Chaturvedi. "Sustaining Fairness via Incremental Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 6 (26 de junho de 2023): 6797–805. http://dx.doi.org/10.1609/aaai.v37i6.25833.
Texto completo da fontePerello, Nick, e Przemyslaw Grabowicz. "Fair Machine Learning Post Affirmative Action". ACM SIGCAS Computers and Society 52, n.º 2 (setembro de 2023): 22. http://dx.doi.org/10.1145/3656021.3656029.
Texto completo da fonteOneto, Luca. "Learning fair models and representations". Intelligenza Artificiale 14, n.º 1 (17 de setembro de 2020): 151–78. http://dx.doi.org/10.3233/ia-190034.
Texto completo da fonteKim, Yun-Myung. "Data and Fair use". Korea Copyright Commission 141 (30 de março de 2023): 5–53. http://dx.doi.org/10.30582/kdps.2023.36.1.5.
Texto completo da fonteKim, Yun-Myung. "Data and Fair use". Korea Copyright Commission 141 (30 de março de 2023): 5–53. http://dx.doi.org/10.30582/kdps.2023.36.1.5.
Texto completo da fonteZhang, Xueru, Mohammad Mahdi Khalili e Mingyan Liu. "Long-Term Impacts of Fair Machine Learning". Ergonomics in Design: The Quarterly of Human Factors Applications 28, n.º 3 (25 de outubro de 2019): 7–11. http://dx.doi.org/10.1177/1064804619884160.
Texto completo da fonteZhu, Yunlan. "The Comparative Analysis of Fair Use of Works in Machine Learning". SHS Web of Conferences 178 (2023): 01015. http://dx.doi.org/10.1051/shsconf/202317801015.
Texto completo da fonteRedko, Ievgen, e Charlotte Laclau. "On Fair Cost Sharing Games in Machine Learning". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julho de 2019): 4790–97. http://dx.doi.org/10.1609/aaai.v33i01.33014790.
Texto completo da fonteLee, Joshua, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory W. Wornell, Leonid Karlinsky e Rogerio Schmidt Feris. "A Maximal Correlation Framework for Fair Machine Learning". Entropy 24, n.º 4 (26 de março de 2022): 461. http://dx.doi.org/10.3390/e24040461.
Texto completo da fontevan Berkel, Niels, Jorge Goncalves, Danula Hettiachchi, Senuri Wijenayake, Ryan M. Kelly e Vassilis Kostakos. "Crowdsourcing Perceptions of Fair Predictors for Machine Learning". Proceedings of the ACM on Human-Computer Interaction 3, CSCW (7 de novembro de 2019): 1–21. http://dx.doi.org/10.1145/3359130.
Texto completo da fonteJEONG, JIN KEUN. "Will the U.S. Court Judge TDM for Artificial Intelligence Machine Learning as Fair Use?" Korea Copyright Commission 144 (31 de dezembro de 2023): 215–50. http://dx.doi.org/10.30582/kdps.2023.36.4.215.
Texto completo da fonteEdwards, Chris. "AI Struggles with Fair Use". New Electronics 56, n.º 9 (setembro de 2023): 40–41. http://dx.doi.org/10.12968/s0047-9624(24)60063-5.
Texto completo da fonteJang, Taeuk, Feng Zheng e Xiaoqian Wang. "Constructing a Fair Classifier with Generated Fair Data". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 9 (18 de maio de 2021): 7908–16. http://dx.doi.org/10.1609/aaai.v35i9.16965.
Texto completo da fonteChandra, Rushil, Karun Sanjaya, AR Aravind, Ahmed Radie Abbas, Ruzieva Gulrukh e T. S. Senthil kumar. "Algorithmic Fairness and Bias in Machine Learning Systems". E3S Web of Conferences 399 (2023): 04036. http://dx.doi.org/10.1051/e3sconf/202339904036.
Texto completo da fonteBrotcke, Liming. "Time to Assess Bias in Machine Learning Models for Credit Decisions". Journal of Risk and Financial Management 15, n.º 4 (5 de abril de 2022): 165. http://dx.doi.org/10.3390/jrfm15040165.
Texto completo da fonteTian, Xiao, Rachael Hwee Ling Sim, Jue Fan e Bryan Kian Hsiang Low. "DeRDaVa: Deletion-Robust Data Valuation for Machine Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 14 (24 de março de 2024): 15373–81. http://dx.doi.org/10.1609/aaai.v38i14.29462.
Texto completo da fontePlečko, Drago, e Elias Bareinboim. "Causal Fairness Analysis: A Causal Toolkit for Fair Machine Learning". Foundations and Trends® in Machine Learning 17, n.º 3 (2024): 304–589. http://dx.doi.org/10.1561/2200000106.
Texto completo da fonteSun, Shao Chao, e Dao Huang. "A Novel Robust Smooth Support Vector Machine". Applied Mechanics and Materials 148-149 (dezembro de 2011): 1438–41. http://dx.doi.org/10.4028/www.scientific.net/amm.148-149.1438.
Texto completo da fonteFirestone, Chaz. "Performance vs. competence in human–machine comparisons". Proceedings of the National Academy of Sciences 117, n.º 43 (13 de outubro de 2020): 26562–71. http://dx.doi.org/10.1073/pnas.1905334117.
Texto completo da fonteLangenberg, Anna, Shih-Chi Ma, Tatiana Ermakova e Benjamin Fabian. "Formal Group Fairness and Accuracy in Automated Decision Making". Mathematics 11, n.º 8 (7 de abril de 2023): 1771. http://dx.doi.org/10.3390/math11081771.
Texto completo da fonteTaylor, Greg. "Risks Special Issue on “Granular Models and Machine Learning Models”". Risks 8, n.º 1 (30 de dezembro de 2019): 1. http://dx.doi.org/10.3390/risks8010001.
Texto completo da fonteDavis, Jenny L., Apryl Williams e Michael W. Yang. "Algorithmic reparation". Big Data & Society 8, n.º 2 (julho de 2021): 205395172110448. http://dx.doi.org/10.1177/20539517211044808.
Texto completo da fonteDavis, Jenny L., Apryl Williams e Michael W. Yang. "Algorithmic reparation". Big Data & Society 8, n.º 2 (julho de 2021): 205395172110448. http://dx.doi.org/10.1177/20539517211044808.
Texto completo da fonteDhabliya, Dharmesh, Sukhvinder Singh Dari, Anishkumar Dhablia, N. Akhila, Renu Kachhoria e Vinit Khetani. "Addressing Bias in Machine Learning Algorithms: Promoting Fairness and Ethical Design". E3S Web of Conferences 491 (2024): 02040. http://dx.doi.org/10.1051/e3sconf/202449102040.
Texto completo da fonteChowdhury, Somnath Basu Roy, e Snigdha Chaturvedi. "Learning Fair Representations via Rate-Distortion Maximization". Transactions of the Association for Computational Linguistics 10 (2022): 1159–74. http://dx.doi.org/10.1162/tacl_a_00512.
Texto completo da fonteAhire, Pritam, Atish Agale e Mayur Augad. "Machine Learning for Forecasting Promotions". International Journal of Science and Healthcare Research 8, n.º 2 (25 de maio de 2023): 329–33. http://dx.doi.org/10.52403/ijshr.20230242.
Texto completo da fonteHeidrich, Louisa, Emanuel Slany, Stephan Scheele e Ute Schmid. "FairCaipi: A Combination of Explanatory Interactive and Fair Machine Learning for Human and Machine Bias Reduction". Machine Learning and Knowledge Extraction 5, n.º 4 (18 de outubro de 2023): 1519–38. http://dx.doi.org/10.3390/make5040076.
Texto completo da fonteTae, Ki Hyun, Hantian Zhang, Jaeyoung Park, Kexin Rong e Steven Euijong Whang. "Falcon: Fair Active Learning Using Multi-Armed Bandits". Proceedings of the VLDB Endowment 17, n.º 5 (janeiro de 2024): 952–65. http://dx.doi.org/10.14778/3641204.3641207.
Texto completo da fonteFitzsimons, Jack, AbdulRahman Al Ali, Michael Osborne e Stephen Roberts. "A General Framework for Fair Regression". Entropy 21, n.º 8 (29 de julho de 2019): 741. http://dx.doi.org/10.3390/e21080741.
Texto completo da fonteKhan, Shahid, Viktor Klochkov, Olha Lavoryk, Oleksii Lubynets, Ali Imdad Khan, Andrea Dubla e Ilya Selyuzhenkov. "Machine Learning Application for Λ Hyperon Reconstruction in CBM at FAIR". EPJ Web of Conferences 259 (2022): 13008. http://dx.doi.org/10.1051/epjconf/202225913008.
Texto completo da fonteSingh, Vivek K., e Kailash Joshi. "Integrating Fairness in Machine Learning Development Life Cycle: Fair CRISP-DM". e-Service Journal 14, n.º 2 (dezembro de 2022): 1–24. http://dx.doi.org/10.2979/esj.2022.a886946.
Texto completo da fonteWei, Jingrui, e Paul M. Voyles. "Foundry-ML: a Platform for FAIR Machine Learning in Materials Science". Microscopy and Microanalysis 29, Supplement_1 (22 de julho de 2023): 720. http://dx.doi.org/10.1093/micmic/ozad067.355.
Texto completo da fonteGaikar, Asha, Dr Uttara Gogate e Amar Panchal. "Review on Evaluation of Stroke Prediction Using Machine Learning Methods". International Journal for Research in Applied Science and Engineering Technology 11, n.º 4 (30 de abril de 2023): 1011–17. http://dx.doi.org/10.22214/ijraset.2023.50262.
Texto completo da fonteGuo, Zhihao, Shengyuan Chen, Xiao Huang, Zhiqiang Qian, Chunsing Yu, Yan Xu e Fang Ding. "Fair Benchmark for Unsupervised Node Representation Learning". Algorithms 15, n.º 10 (17 de outubro de 2022): 379. http://dx.doi.org/10.3390/a15100379.
Texto completo da fonteAmpountolas, Apostolos, Titus Nyarko Nde, Paresh Date e Corina Constantinescu. "A Machine Learning Approach for Micro-Credit Scoring". Risks 9, n.º 3 (9 de março de 2021): 50. http://dx.doi.org/10.3390/risks9030050.
Texto completo da fonteZhang, Yixuan, Boyu Li, Zenan Ling e Feng Zhou. "Mitigating Label Bias in Machine Learning: Fairness through Confident Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 15 (24 de março de 2024): 16917–25. http://dx.doi.org/10.1609/aaai.v38i15.29634.
Texto completo da fonteMenezes, Andreia Duarte, Edilberto Pereira Teixeira, Jose Roberto Delalibera Finzer e Rafael Bonacin de Oliveira. "Machine learning-driven development of niobium-containing optical glasses". Research, Society and Development 11, n.º 9 (5 de julho de 2022): e13811931290. http://dx.doi.org/10.33448/rsd-v11i9.31290.
Texto completo da fonteAsher, Nicholas, Lucas De Lara, Soumya Paul e Chris Russell. "Counterfactual Models for Fair and Adequate Explanations". Machine Learning and Knowledge Extraction 4, n.º 2 (31 de março de 2022): 316–49. http://dx.doi.org/10.3390/make4020014.
Texto completo da fonteMohsin, Farhad, Ao Liu, Pin-Yu Chen, Francesca Rossi e Lirong Xia. "Learning to Design Fair and Private Voting Rules". Journal of Artificial Intelligence Research 75 (30 de novembro de 2022): 1139–76. http://dx.doi.org/10.1613/jair.1.13734.
Texto completo da fonteGoretzko, David, e Laura Sophia Finja Israel. "Pitfalls of Machine Learning-Based Personnel Selection". Journal of Personnel Psychology 21, n.º 1 (janeiro de 2022): 37–47. http://dx.doi.org/10.1027/1866-5888/a000287.
Texto completo da fonteYugam Bajaj and Shallu Bashambu. "Traffic Signs Detection Using Machine Learning Algorithms". November 2020 6, n.º 11 (23 de novembro de 2020): 109–12. http://dx.doi.org/10.46501/ijmtst061119.
Texto completo da fonteZhao, Yanqi, Yong Yu, Yannan Li, Gang Han e Xiaojiang Du. "Machine learning based privacy-preserving fair data trading in big data market". Information Sciences 478 (abril de 2019): 449–60. http://dx.doi.org/10.1016/j.ins.2018.11.028.
Texto completo da fonteLuo, Xi, Ran Yan, Shuaian Wang e Lu Zhen. "A fair evaluation of the potential of machine learning in maritime transportation". Electronic Research Archive 31, n.º 8 (2023): 4753–72. http://dx.doi.org/10.3934/era.2023243.
Texto completo da fonteMudarakola, Lakshmi Prasad, D. Shabda Prakash, K. L. N. Shashidhar e D. Yaswanth. "Car Price Prediction Using Machine Learning". International Journal for Research in Applied Science and Engineering Technology 12, n.º 5 (31 de maio de 2024): 81–87. http://dx.doi.org/10.22214/ijraset.2024.61441.
Texto completo da fonteBuijs, Maria Magdalena, Mohammed Hossain Ramezani, Jürgen Herp, Rasmus Kroijer, Morten Kobaek-Larsen, Gunnar Baatrup e Esmaeil S. Nadimi. "Assessment of bowel cleansing quality in colon capsule endoscopy using machine learning: a pilot study". Endoscopy International Open 06, n.º 08 (agosto de 2018): E1044—E1050. http://dx.doi.org/10.1055/a-0627-7136.
Texto completo da fonteCovaci, Florina. "Machine Learning Empowered Insights into Rental Market Behavior". Journal of Economics, Finance and Accounting Studies 6, n.º 2 (23 de abril de 2024): 143–55. http://dx.doi.org/10.32996/jefas.2024.6.2.11.
Texto completo da fontePemmaraju Satya Prem. "Machine learning in employee performance evaluation: A HRM perspective". International Journal of Science and Research Archive 11, n.º 1 (28 de fevereiro de 2024): 1573–85. http://dx.doi.org/10.30574/ijsra.2024.11.1.0193.
Texto completo da fonteLakshmi, Metta Dhana, Jani Revathi, Chichula Sravani, Maddila Adarsa Suhas e Balagam Umesh. "Comparative Analysis of Ride-On-Demand Services for Fair Price Detection Using Machine Learning". International Journal for Research in Applied Science and Engineering Technology 12, n.º 4 (30 de abril de 2024): 2557–66. http://dx.doi.org/10.22214/ijraset.2024.60337.
Texto completo da fonteChakraborty, Pratic. "Embedded Machine Learning and Embedded Systems in the Industry." International Journal for Research in Applied Science and Engineering Technology 9, n.º 11 (30 de novembro de 2021): 1872–75. http://dx.doi.org/10.22214/ijraset.2021.39067.
Texto completo da fonteFazelpour, Sina, e Maria De-Arteaga. "Diversity in sociotechnical machine learning systems". Big Data & Society 9, n.º 1 (janeiro de 2022): 205395172210820. http://dx.doi.org/10.1177/20539517221082027.
Texto completo da fonte