چکیده مقاله
Ensuring fairness in recruitment has become a critical challenge in modern organizations, particularly with the increasing adoption of Artificial Intelligence AI in decision making This paper proposes a hybrid framework that integrates Convolutional Neural Networks CNNs with Support Vector Machines SVMs to address bias in recruitment processes The CNN component is employed for feature extraction from candidate profiles, including textual and image based data, while the SVM is utilized for classification and fairness adjustment To mitigate algorithmic bias, fairness aware constraints are incorporated into the training phase, ensuring equitable outcomes across demographic groups Experimental results on benchmark recruitment datasets demonstrate that the proposed CNN SVM model significantly improves fairness metrics such as Demographic Party DP and Equal Opportunity EO without compromising classification accuracy This approach highlights the potential of combining deep learning and traditional machine learning models to promote transparent, unbiased, and effective recruitment practices in real world applications
کلیدواژهها
نویسندگان
Razieh Zakouei
Semnan University Faculty of Economics, Management and Administrative Sciences, Iran
شیوه ارجاع
Zakouei, Razieh,1404,Fairness-Aware Recruitment Using Convolutional Neural Networks Combined with Support Vector Machines,22th International Conference on Innovation and Research in Engineering Sciences (ICIRES)
ارائهشده در
مجموعه مقالات بیست و دومین کنفرانس بین المللی نوآوری و تحقیق در علوم مهندسی7 آذر 1404