#Adversarial Robustness in Machine Learning

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

A ‘biased’ emerging governance regime for artificial intelligence? How AI ethics get skewed moving from …

AI Governance by Human Rights-Centred Design, Deliberation and Oversight: An End to Ethics Washing

Copyright Protection and Accountability of Generative AI: Attack, Watermarking and Attribution

Generative AI (e.g., Generative Adversarial Networks - GANs) has become increasingly popular in recent years. However, Generative AI introduces significant concerns regarding the protection of …

Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions

Artificial intelligence (AI) research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the …

Conversational Explanations of Machine Learning Predictions Through Class-contrastive Counterfactual Statements

Machine learning models have become pervasive in our everyday life; they decide on important matters influencing our education, employment and judicial system. Many of these …

Protecting Intellectual Property With Reliable Availability of Learning Models in AI-Based Cybersecurity Services

Artificial intelligence (AI)-based cybersecurity services offer significant promise in many scenarios, including malware detection, content supervision, and so on. Meanwhile, many commercial and government applications …

Privacy-Preserving Models for Legal Natural Language Processing

Pre-training large transformer models with in-domain data improves domain adaptation and helps gain performance on the domain-specific downstream tasks. However, sharing models pre-trained on potentially …

Overcoming Barriers to Cross-cultural Cooperation in AI Ethics and Governance

Abstract Achieving the global benefits of artificial intelligence (AI) will require international cooperation on many areas of governance and ethical standards, while allowing for diverse …

In Defence of Principlism in AI Ethics and Governance

Protecting Intellectual Property of Deep Neural Networks with Watermarking

Deep learning technologies, which are the key components of state-of-the-art Artificial Intelligence (AI) services, have shown great success in providing human-level capabilities for a variety …

Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned

Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learned models and data-driven systems, and …