A5092829348- Explainable Artificial Intelligence (XAI)
- Ethics and Social Impacts of AI
- Adversarial Robustness in Machine Learning
- Crime Patterns and Interventions
- Artificial Intelligence in Healthcare and Education
- Machine Learning and Data Classification
- Imbalanced Data Classification Techniques
- Cybercrime and Law Enforcement Studies
- Online Learning and Analytics
- Anomaly Detection Techniques and Applications
University College London
2024
Abstract This systematic literature review investigates the fairness of machine learning algorithms in educational settings, focusing on recent studies and their proposed solutions to address biases. Applications analyzed include student dropout prediction, performance forum post classification, recommender systems. We identify common strategies, such as adjusting sample weights, bias attenuation methods, through un/awareness, adversarial learning. Commonly used metrics for assessment...
This research investigates bias in AI algorithms used for monitoring student progress, specifically focusing on related to age, disability, and gender. The study is motivated by incidents such as the UK A-level grading controversy, which demonstrated real-world implications of biased algorithms. Using Open University Learning Analytics Dataset, evaluates fairness with metrics like ABROCA, Average Odds Difference, Equality Opportunity Difference. analysis structured into three experiments....
Abstract The increasing use of algorithms in predictive policing has raised concerns regarding the potential amplification societal biases. This study adopts a two-phase approach, encompassing systematic review and mitigation age-related biases policing. Our identifies variety fairness strategies existing literature, such as domain knowledge, likelihood function penalties, counterfactual reasoning, demographic segmentation, with primary focus on racial However, this also highlights...
As AI models are increasingly deployed in critical applications, ensuring the consistent performance of when exposed to unusual situations such as out-of-distribution (OOD) or perturbed data, is important. Therefore, this paper investigates uncertainty various deep neural networks, including ResNet-50, VGG16, DenseNet121, AlexNet, and GoogleNet, dealing with data. Our approach includes three experiments. First, we used pretrained classify OOD images generated via DALL-E assess their...