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Dynamic algorithmic awareness based on FAT evaluation: Heuristic intervention and multidimensional prediction.

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Titel: Dynamic algorithmic awareness based on FAT evaluation: Heuristic intervention and multidimensional prediction.
Autoren: Liu, Jing1, Wu, Dan2,3 woodan@whu.edu.cn, Sun, Guoye2, Deng, Yuyang2
Quelle: Journal of the Association for Information Science & Technology. Apr2025, Vol. 76 Issue 4, p718-739. 22p.
Schlagwörter: *Cluster analysis (Statistics), *Privacy, *Content analysis, *Experimental design, *Research methodology, *Machine learning, *Algorithms, *User interfaces, Intellect, Random forest algorithms, Boosting algorithms, Prediction models, Research funding, Responsibility, Logistic regression analysis, Consumer attitudes, Interviewing, Dimensional reduction algorithms, Descriptive statistics, Pre-tests & post-tests, Support vector machines, Industrial research, One-way analysis of variance, Comparative studies, College students, Data analysis software, Medical ethics
Geografische Kategorien: China
Abstract: As the widespread use of algorithms and artificial intelligence (AI) technologies, understanding the interaction process of human–algorithm interaction becomes increasingly crucial. From the human perspective, algorithmic awareness is recognized as a significant factor influencing how users evaluate algorithms and engage with them. In this study, a formative study identified four dimensions of algorithmic awareness: conceptions awareness (AC), data awareness (AD), functions awareness (AF), and risks awareness (AR). Subsequently, we implemented a heuristic intervention and collected data on users' algorithmic awareness and FAT (fairness, accountability, and transparency) evaluation in both pre‐test and post‐test stages (N = 622). We verified the dynamics of algorithmic awareness and FAT evaluation through fuzzy clustering and identified three patterns of FAT evaluation changes: "Stable high rating pattern," "Variable medium rating pattern," and "Unstable low rating pattern." Using the clustering results and FAT evaluation scores, we trained classification models to predict different dimensions of algorithmic awareness by applying different machine learning techniques, namely Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and XGBoost (XGB). Comparatively, experimental results show that the SVM algorithm accomplishes the task of predicting the four dimensions of algorithmic awareness with better results and interpretability. Its F1 scores are 0.6377, 0.6780, 0.6747, and 0.75. These findings hold great potential for informing human‐centered algorithmic practices and HCI design. [ABSTRACT FROM AUTHOR]
Datenbank: Library, Information Science & Technology Abstracts
Beschreibung
Abstract:As the widespread use of algorithms and artificial intelligence (AI) technologies, understanding the interaction process of human–algorithm interaction becomes increasingly crucial. From the human perspective, algorithmic awareness is recognized as a significant factor influencing how users evaluate algorithms and engage with them. In this study, a formative study identified four dimensions of algorithmic awareness: conceptions awareness (AC), data awareness (AD), functions awareness (AF), and risks awareness (AR). Subsequently, we implemented a heuristic intervention and collected data on users' algorithmic awareness and FAT (fairness, accountability, and transparency) evaluation in both pre‐test and post‐test stages (N = 622). We verified the dynamics of algorithmic awareness and FAT evaluation through fuzzy clustering and identified three patterns of FAT evaluation changes: "Stable high rating pattern," "Variable medium rating pattern," and "Unstable low rating pattern." Using the clustering results and FAT evaluation scores, we trained classification models to predict different dimensions of algorithmic awareness by applying different machine learning techniques, namely Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and XGBoost (XGB). Comparatively, experimental results show that the SVM algorithm accomplishes the task of predicting the four dimensions of algorithmic awareness with better results and interpretability. Its F1 scores are 0.6377, 0.6780, 0.6747, and 0.75. These findings hold great potential for informing human‐centered algorithmic practices and HCI design. [ABSTRACT FROM AUTHOR]
ISSN:23301635
DOI:10.1002/asi.24969