This article provides a comprehensive overview of the fairness issues in artificial intelligence (AI) systems, delving into its background, definition, and development process. The article explores the fairness problem in AI through practical applications and current advances and focuses on bias analysis and fairness training as key research directions. The paper explains in detail the concept, implementation, characteristics, and use cases of each method. The paper explores strategies to reduce bias and improve fairness in AI systems, reviews challenges and solutions to real-world AI fairness applications, and proposes future research directions. In addition, this study provides an in-depth comparative analysis of the various approaches, utilizing cutting-edge research information to elucidate their different characteristics, strengths, and weaknesses. The results of the comparison provide guidance for future research. The paper concludes with an overview of existing challenges in practical applications and suggests priorities and solutions for future research. The conclusions provide insights for promoting fairness in AI systems. The information reviewed in this paper is drawn from reputable sources, including leading academic journals, prominent conference proceedings, and well-established online repositories dedicated to AI fairness. However, it is important to recognize that research nuances, sample sizes, and contextual factors may create limitations that affect the generalizability of the findings.

Feature selection is an important issue in the field of machine learning, which can reduce misleading computations and improve classification performance. Generally, feature selection can be considered as a binary optimization problem. Gravitational Search Algorithm (GSA) is a population-based heuristic algorithm inspired by Newton’s laws of gravity and motion. Although GSA shows good performance in solving optimization problems, it has a shortcoming of premature convergence. In this paper, the concept of global memory is introduced and the definition of exponential $Kbest$ is used in an improved version of GSA called IGSA. In this algorithm, the position of the optimal solution obtained so far is memorized, which can effectively prevent particles from gathering together and moving slowly. In this way, the exploitation ability of the algorithm gets improved, and a proper balance between exploration and exploitation gets established. Besides, the exponential $Kbest$ can significantly decrease the running time. In order to solve feature selection problem, a binary IGSA (BIGSA) is further introduced. The proposed algorithm is tested on a set of standard datasets and compared with other algorithms. The experimental results confirm the high efficiency of BIGSA for feature selection.

The dual hesitant fuzzy sets (DHFSs) were proposed by Zhu et al. (2012), which encompass fuzzy sets, intuitionistic fuzzy sets, hesitant fuzzy sets, and fuzzy multisets as special cases. Correlation measures analysis is an important research topic. In this paper, we define the correlation measures for dual hesitant fuzzy information and then discuss their properties in detail. One numerical example is provided to illustrate these correlation measures. Then we present a direct transfer algorithm with respect to the problem of complex operation of matrix synthesis when reconstructing an equivalent correlation matrix for clustering DHFSs. Furthermore, we prove that the direct transfer algorithm is equivalent to transfer closure algorithm, but its asymptotic time complexity and space complexity are superior to the latter. Another real world example, that is, diamond evaluation and classification, is employed to show the effectiveness of the association coefficient and the algorithm for clustering DHFSs.

The communication relationship can reflect the behavior relationship between different communication targets. The in-depth analysis of the communication relationship can obtain the behaviors of communication individuals, and speculate their hierarchical positions in the communication network, so as to provide a basis for further speculation on the structure of the communication network. For massive spectrum signals, we can also obtain important information such as communication relationships and behaviors of communication individuals, without cracking the signal content, but by analyzing the physical characteristics and statistical laws of the spectrum signals. In order to overcome the difficulties and costs of analyzing communication behaviors from cracking the signal content in existing research, this paper studies the physical characteristics and statistical laws of spectrum signals based on the features of frequency hopping period, average power and time of signal occurrence. Because the spectrum signals generated by the communication individuals show clustering characteristics, this paper proposes a communication relationship mining method based on improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise). The method can accurately discover the communication relationship of the radio station from the incomplete spectrum monitoring data, without cracking the content carried by the spectrum signals, which provides a new idea for the mining and analysis of mass spectrum monitoring signals.

This paper focuses on the security and robustness of smart campuses by studying a virus propagation model. Establishing a model that reflects the epidemiological transmission mechanism is a primary method for studying disease outbreaks and control. However, the relationship between microscopic and macrodynamic evolution in networks has long remained an unsolved problem. The existing virus transmission models, which ignore differences between individuals, cannot objectively reflect the spread of infectious diseases. In this paper, a differential model of virus propagation is established for a smart campus network by considering the differences among individuals, and a method to extend the individual evolution process to the evolution process of the entire network is proposed. First, the functional process of a virus infection in individuals is reflected by the evolution model of a single node. Then, we extend the individual evolution to the whole network to reflect the scale of virus transmission and propagation among individuals in a smart campus network. The proposed model more objectively reflects viral infections and could serve to enhance the security and robustness of smart campuses.

Gravitational search algorithm (GSA) is a population-based heuristic algorithm, which is inspired by Newton’s laws of gravity and motion. Although GSA provides a good performance in solving optimization problems, it has a disadvantage of premature convergence. In this paper, the concept of repulsive force is introduced and the definition of exponential $Kbest$ is used in a new version of GSA, which is called repulsive GSA with exponential $Kbest$ (EKRGSA). In this algorithm, heavy particles repulse or attract all particles according to distance, and all particles search the solution space under the combined action of repulsive force and gravitational force. In this way, the exploration ability of the algorithm is improved and a proper balance between exploration and exploitation is established. Moreover, the exponential $Kbest$ significantly decreases the computational time. The proposed algorithm is tested on a set of benchmark functions and compared with other algorithms. The experimental results confirm the high efficiency of EKRGSA.