A Review of Bias and Fairness in Artificial Intelligence by Rubén González-Sendino, Emilio Serrano, Javier Bajo, Paulo Novais

“…As a result, a new field of research on algorithmic fairness has emerged. In this area, detecting biases and mitigating them is essential to ensure fair and discrimination-free decisions. …”
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A Comprehensive Review of Facial Beauty Prediction Using Multi-task Learning and Facial Attributes by Ali H. Ibrahem, Adnan M. Abdulazeez

“…We further examine the methodological challenges in defining and measuring beauty, such as data imbalances and algorithmic fairness. By synthesizing insights from psychology and machine learning, this work highlights the potential of interdisciplinary approaches to enhance the reliability and inclusivity of automated beauty prediction systems. …”
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Research on Solving Path of Negative Effect of “Information Cocoon Room” in Emergency by Wei Liu, Wei Zhou

“…By optimizing information ontology, using technology for good governance, promoting algorithmic fairness, and enhancing social stickiness, we help the public break through the shackles and negative effects of the information cocoon and promote the harmonious, stable, and healthy development of the emergency information field.…”
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A Novel Scheduling Algorithm Based on Game Theory and Multicriteria Decision Making in LTE Network by Mohammad Nour Hindia, Ahmed Wasif Reza, Kamarul Ariffin Noordin

“…On the first level, bankruptcy and shapely value algorithm fairly distribute the resources among smart grid applications. …”
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Effective and fair cognitive terminal assignment scheme for cooperative spectrum sensing by WU Zhiqiang, LIU Qianli, LIU Jiabin, FENG Qing, XIAO Shanpeng, LIU Shang

“…For the perceived fairness among different sub-bands, two allocation algorithms were proposed: the arcuate allocation algorithm and the class division allocation algorithm. Fairness between sub-bands was measured by evaluating the worst perceived performance in the sub-band. …”
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Predicting pediatric patient rehabilitation outcomes after spinal deformity surgery with artificial intelligence by Wenqi Shi, Felipe O. Giuste, Yuanda Zhu, Ben J. Tamo, Micky C. Nnamdi, Andrew Hornback, Ashley M. Carpenter, Coleman Hilton, Henry J. Iwinski, J. Michael Wattenbarger, May D. Wang

“…Moreover, we enable responsible AI by calibrating model confidence for human intervention and mitigating health disparities for algorithm fairness. Results The best prediction model achieves an area under receiver operating curve (AUROC) performance of 0.86, 0.85, and 0.83 for individual SRS-22R question response prediction over three-time horizons from pre-operation to 6-month, 1-year, and 2-year post-operation, respectively. …”
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