AINeutralarXiv – CS AI · 4d ago6/10
🧠Researchers propose GDGU, a machine learning technique that enables electric vehicle charging stations to delete training data from deployed cyberattack detection models without full retraining, addressing privacy regulations while maintaining security effectiveness. The method achieves comparable performance to stronger baselines while being 10-12 times faster and more memory-efficient than retraining from scratch.
AINeutralarXiv – CS AI · Jun 106/10
🧠A comprehensive empirical study examines how German software engineers adopt generative AI tools, revealing that experience level, organizational size, and lack of project context awareness significantly influence effectiveness. The research combines 18 interviews with 109 survey responses to identify adoption patterns and barriers in a regulatory-constrained environment.
AINeutralarXiv – CS AI · Jun 86/10
🧠Researchers propose Analytic Continual Unlearning (ACU), a gradient-free method enabling efficient removal of specific knowledge from pre-trained models during continuous learning phases while preserving privacy. The approach uses closed-form solutions to handle sequential forgetting requests, addressing gaps in existing unlearning techniques that struggle with privacy violations and adversarial request patterns.
AINeutralarXiv – CS AI · Jun 46/10
🧠Researchers present a framework for exact unlearning in reinforcement learning that enables efficient removal of user data upon request, with computational costs only a ρ√ln T fraction of full retraining. The work establishes both an algorithm achieving near-optimal regret bounds for tabular MDPs and matching lower bounds, advancing the theoretical foundation for privacy-preserving machine learning systems.
AINeutralarXiv – CS AI · May 276/10
🧠Researchers introduce Shadow Unlearning, a privacy-preserving machine unlearning method that removes training data influence from LLMs without exposing sensitive information to attacks. The Neuro-Semantic Projector Unlearning (NSPU) framework achieves this while maintaining model performance and is 10x more computationally efficient than existing approaches.
AIBullisharXiv – CS AI · May 126/10
🧠Researchers have developed GLiNER2-PII, a compact 0.3B-parameter multilingual model for detecting personally identifiable information across 42 entity types at character-level precision. Trained on a synthetic corpus of 4,910 annotated texts to overcome privacy constraints in real data collection, the model outperforms existing systems including OpenAI's Privacy Filter on benchmark evaluations and is now publicly available on Hugging Face.
🏢 OpenAI🏢 Hugging Face
AINeutralarXiv – CS AI · Apr 156/10
🧠Researchers introduce a sequential unlearning framework that enables Large Language Models to forget sensitive data while maintaining performance, addressing GDPR compliance and the Right to be Forgotten in politically sensitive deployments. The method stabilizes general capabilities through positive fine-tuning before selectively suppressing designated patterns, demonstrating effectiveness on the SemEval-2025 benchmark with minimal accuracy degradation.
AINeutralarXiv – CS AI · Apr 106/10
🧠Researchers propose AdaProb, a machine unlearning method that enables trained AI models to efficiently forget specific data while preserving privacy and complying with regulations like GDPR. The approach uses adaptive probability distributions and demonstrates 20% improvement in forgetting effectiveness with 50% less computational overhead compared to existing methods.
AIBullisharXiv – CS AI · Mar 176/10
🧠Researchers propose a dual-path AI framework combining Variational Autoencoders and Wasserstein GANs for real-time fraud detection in banking systems. The system achieves sub-50ms detection latency while maintaining GDPR compliance through selective explainability mechanisms for high-uncertainty transactions.
