AI Sec Watch

This is a survey article that reviews research on selective forgetting in machine learning, which is the ability to remove or reduce specific information from a trained AI model without completely retraining it from scratch. The article covers methods and applications of this technique across various AI systems and domains. The survey appears to be an academic overview of current knowledge in this area rather than describing a specific problem or vulnerability.

This academic review examines how bias (systematic unfairness in AI decision-making) occurs in AI systems and explores the human roles, solutions, and research methods used to identify and reduce it. The paper surveys existing approaches to addressing bias rather than proposing a single new solution.

This is an academic survey article published in ACM Computing Surveys that discusses a question bank designed to help assess risks in AI systems responsibly. The article appears to be a comprehensive review of how organizations can evaluate potential harms and safety concerns when developing or deploying AI, rather than describing a specific vulnerability or problem.

This academic paper is a systematic review published in ACM Computing Surveys that examines how trust works in artificial intelligence systems using established trust theory frameworks. The article analyzes trust in AI through theoretical lenses rather than addressing a specific technical vulnerability or problem.

This survey article reviews methods for detecting training data used to build large language models (LLMs, which are AI systems trained on massive amounts of text to generate human-like responses). The paper examines various techniques that researchers have developed to identify and extract information about what data was used to train these models, which is important for understanding model behavior and potential privacy concerns.

This research paper studies whether deepfake detection systems (AI tools that identify fake videos made to look real) are fair across different groups of people based on ethnicity, gender, and age. The study found that these detection systems often perform differently depending on the person's background, meaning they work better for some groups than others. The paper highlights that bias in deepfake detection is an important fairness problem that needs attention.

This academic paper discusses using explainable AI (AI systems that can show their reasoning for decisions) to detect financial fraud as it happens in real time. The research focuses on making fraud detection systems that adapt to new fraud patterns while also being transparent about why they flag transactions as suspicious.

This research paper proposes a new method for detecting deepfakes (AI-generated fake videos or images) by using neural networks (computer systems loosely modeled on how brains learn) trained with both current and quantum computing approaches. The dual approach aims to make deepfake detection more reliable and harder for attackers to bypass.

This research article proposes a hybrid machine learning approach to improve trust management and security in mobile crowdsourcing (a system where mobile users contribute data or complete tasks for a distributed project). The study combines multiple machine learning techniques to identify trustworthy participants and protect against malicious actors in crowdsourcing environments.