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Bias analysis of AI models for undergraduate student admissions

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Neural Computing and Applications 2025年第12期37卷 7785-7795页

作者： Van Busum, Kelly Fang, Shiaofen Department of Computer Science and Software Engineering Butler University IndianapolisIN United States Computing and Engineering Indiana University Luddy School of Informatics Indiana University Indianapolis IndianapolisIN United States

Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors Van Busum and Fang (Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 2023) to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a given student would be directly admitted into the School of Science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed AI models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We also extended our analysis to show that the biases detected were persistent. Finally, we included several fairness metrics in our analysis and discussed the uses and limitations of these metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Students

Enhancing Intent Detection Through ChatGPT-Driven Data Augmentation 5th

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Enhancing Intent Detection Through ChatGPT-Driven Data Augme...

5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Tesfagergish, Senait Gebremichael Damasevicius, Robertas Kapociute-Dzikiene, Jurgita Department of Software Engineering Kaunas University of Technology Kaunas Lithuania Department of Applied Informatics Vytautas Magnus University Kaunas Lithuania

ISBN: (纸本)9789819771776

This study investigates the improvement of intent detection tasks, focusing on the Facebook Multilingual Task Oriented Dataset, which encompasses 12 classes, including specific intents like weather and alarms. To address the scarcity of datasets in the Amharic language for intent detection, we first translated the English dataset into Amharic using Google Translator. Additionally, we expanded the dataset to include Lithuanian, German, French, and Czech languages. Next, we balance the dataset through data augmentation using ChatGPT, particularly targeting the four least-covered classes by adding 50 sentences to each. For text vectorization, the language-agnostic BERT sentence embedding model was used for its multilingual capabilities and Amharic language support. The classification was based on Cosine similarity, incorporating a majority voting mechanism from the most similar training instances. We conducted multilingual experiments to evaluate the performance of intent detection across various languages, both before and after data augmentation. Our findings highlight the effectiveness of using ChatGPT for data augmentation in intent detection tasks across all investigated languages, particularly in enhancing underrepresented languages and specific intents. We achieved improvement in intent detection performance for minority classes. This shows the benefit of using ChatGPT with imbalanced datasets for multilingual tasks and applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Translation (languages)

Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

AdagradLSPIA: Integrating adaptive optimization into least squares progressive iterative approximation

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arXiv 2025年

作者： Sajavičius, Svajūnas Department of Software Engineering Faculty of Informatics Kaunas University of Technology Lithuania

This paper introduces the Adaptive Gradient Least Squares Progressive iterative Approximation (AdagradLSPIA), an accelerated version of the Least Squares Progressive Iterative Approximation (LSPIA) method, enhanced with adaptive optimization techniques inspired by the adaptive gradient (Adagrad) algorithm. By dynamically adjusting weights using historical gradient information, AdagradLSPIA achieves faster convergence compared to the standard LSPIA method. The method’s effectiveness is demonstrated through tensor product B-spline surface fitting, where it consistently outperforms LSPIA in terms of accuracy, computational efficiency, and robustness to variations in global weight selection. Theoretical analysis confirms its convergence guarantees, and experimental results highlight its suitability for complex surface fitting tasks in geometric modeling and reverse engineering. Copyright © 2025, The Authors. All rights reserved.

关键词： Surface fitting

Money Recognition for the Visually Impaired: A Case Study on Sri Lankan Banknotes

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arXiv 2025年

作者： Bandara, Akshaan Department of Software Engineering Informatics Institute of Technology Colombo Sri Lanka

Currency note recognition is a critical accessibility need for blind individuals, as identifying banknotes accurately can impact their independence and security in financial transactions. Several traditional and technological initiatives have been taken to date. Nevertheless, these approaches are less user-friendly and have made it more challenging for blind people to identify banknotes. This research proposes a user-friendly stand-alone system for the identification of Sri Lankan currency notes. A custom-created dataset of images of Sri Lankan currency notes was used to fine-tune an EfficientDet model. The currency note recognition model achieved 0.9847 AP on the validation dataset and performs exceptionally well in real-world scenarios. The high accuracy and the intuitive interface have enabled blind individuals to quickly and accurately identify currency denominations, ultimately encouraging accessibility and independence. © 2025, CC BY.

关键词： Assistive technology

Structure learning of Bayesian networks using sparrow optimization algorithm

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International Journal of Systematic Innovation

International Journal of Systematic Innovation 2025年第2期9卷 14-25页

作者： Kareem, Shahab Wahhab Awla, Hoshang Qasim Mohammed, Amin Salih Department of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq Department of Computer Technical Engineering Al-Qalam University College Kirkuk Iraq Department of Computer Science Faculty of Science Soran University Soran Iraq Department of Software and Informatics Salahaddin University Erbil Iraq

Bayesian networks are powerful analytical models in machine learning, used to represent probabilistic relationships among variables and create learning structures. These networks are made up of parameters that show conditional probabilities and a structure that shows how random variables interact with each other. The structure is shown by a directed acyclic graph. Despite the NP-hard nature of learning Bayesian network structures, there has been significant progress in improving the accuracy of approximation solutions. The main focus is on score-based search strategies, which make use of functions to evaluate network models and identify structures with high scores. This study is significantly focused on structure learning Bayesian networks using the Bayesian Dirichlet equivalent uniform scoring function and metaheuristic search strategies. To this end, this paper presents the sparrow optimization algorithm (SOA), a new metaheuristic algorithm derived from the foraging behavior of sparrows. SOA performs a concurrent optimization in the solution space by simultaneously performing a local and global search that leads to the discovery of near-optimal structures. The results from our experiments on several benchmark datasets show that SOA yields overall better performance than SA and greedy search algorithms. In particular, it is claimed that by applying the proposed approach of SOA, the convergence speed is significantly higher compared with the existing ones;F1 score is 0.35 and 0.05 for the Hamming distance with better results. Given these results, signed operators prove to be very efficient in SOA’s Bayesian network structure learning as a concept, especially for real-world use. © (2024), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Bayesian Network Global and Local Search Search and Score Sparrow Search Optimization Algorithm Structure Learning

Securing the Silver Surfers: Cybersecurity Awareness Among Older Adults 19

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Securing the Silver Surfers: Cybersecurity Awareness Among O...

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Ahmad Nawi, Haslinda Sutan Omar, Siti Fatimah Basaruddin, Suzana Info. Science & Computing Department Management and Science University Selangor Malaysia Software Engineering Section UniKL MIIT Kuala Lumpur Malaysia Informatics & Analytics Section UniKL MIIT Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

This research investigates the perceptions of cybersecurity among older adults in Malaysia, aged 60 years and above, who are reliant on technology for basic activities. The data from 331 participants showed that 30.8% were online for 4-8 hours daily, while 3.3% were completely disconnected. Interestingly, more than half of the respondents were unaware of cybersecurity threats, especially malware and ransomware, which put their data in jeopardy. While there is some awareness related to unauthorized access and computer fraud, knowledge gaps remain significant, and effective educational interventions are thus needed. Informal, self-directed learning appears to be a widespread way of acquiring skills in this age group, often supplemented by community workshops and peer networks. Given that almost half of the older adults spend considerable time online, improving their digital skills in tandem with their support systems is an important goal for enhancing their online experiences. It indicates the need for a twin approach: enhancing cybersecurity awareness with specialized educational programs, alongside developing flexible opportunities for informal learning. Educational institutions and relevant policies are encouraged to prioritize building hands-on, specific cybersecurity education for seniors. Also, user interface design considering cybersecurity-related issues can make using the technological environment as user-friendly and secure as possible. These strategies will contribute to creating an inclusive digital society, where older adults are confident and safe in using digital technologies while continuously contributing to their personal and professional development. © 2025 IEEE.

关键词： Economic and social effects

Usability Issues With Mobile Applications: Insights From Practitioners and Future Research Directions

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arXiv 2025年

作者： Weichbroth, Pawel Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Software Engineering Gdansk Poland

This study is motivated by two key considerations: the significant benefits mobile applications offer individuals and businesses, and the limited empirical research on usability challenges. To address this gap, we conducted structured interviews with twelve experts to identify common usability issues. Our findings highlight the top five concerns related to: information architecture, user interface design, performance, interaction patterns, and aesthetics. In addition, we identify five key directions for future research: usability in AI-powered mobile applications, augmented reality (AR) and virtual reality (VR), multimodal interactions, personalized mobile ecosystems, and accessibility. Our study provides insights into emerging usability challenges and trends, contributing to both the theory and practice of mobile human-computer interaction. © 2025, CC BY.

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AI and the Law: Evaluating ChatGPT’s Performance in Legal Classification

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arXiv 2025年

作者： Weichbroth, Pawel Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Software Engineering Gdansk Poland

The use of ChatGPT to analyze and classify evidence in criminal proceedings has been a topic of ongoing discussion. However, to the best of our knowledge, this issue has not been studied in the context of the Polish language. This study addresses this research gap by evaluating the effectiveness of ChatGPT in classifying legal cases under the Polish Penal Code. The results show excellent binary classification accuracy, with all positive and negative cases correctly categorized. In addition, a qualitative evaluation confirms that the legal basis provided for each case, along with the relevant legal content, was appropriate. The results obtained suggest that ChatGPT can effectively analyze and classify evidence while applying the appropriate legal rules. In conclusion, ChatGPT has the potential to assist interested parties in the analysis of evidence and serve as a valuable legal resource for individuals with less experience or knowledge in this area. © 2025, CC BY.

关键词： Laws and legislation