Abstract Machine learning models deployed as a service (MLaaS) are susceptible to model
stealing attacks, where an adversary attempts to steal the model within a restricted access …

In this paper, we propose a novel and practical mechanism which enables the service
provider to verify whether a suspect model is stolen from the victim model via model …

Researchers have started to recognize the necessity for a well-defined ML governance
framework based on the principle of decentralization and comprehensively defining its …

The use of black-box models (eg, deep neural networks) in high-stakes decision-making
systems, whose internal logic is complex, raises the need for providing explanations about …

Artificially intelligent (AI) systems have ushered in a transformative era across various
domains, yet their inherent traits of unpredictability, unexplainability, and uncontrollability …

The rapid growth of machine learning (ML) technologies has transformed industries by
enabling data-driven decisionmaking, yet it has also raised critical ethical concerns …

The centralization of Artificial Intelligence (AI) poses significant challenges, including single
points of failure, inherent biases, data privacy concerns, and scalability issues. These …

Early backdoor attacks against machine learning set off an arms race in attack and defence
development. Defences have since appeared demonstrating some ability to detect …

Data is essential to train and fine-tune today's frontier artificial intelligence (AI) models and to
develop future ones. To date, academic, legal, and regulatory work has primarily addressed …

As the implementation of machine learning (ML) systems becomes more widespread,
especially with the introduction of larger ML models, we perceive a spring demand for …