#Adversarial Robustness in Machine Learning

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

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 …

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

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 …

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 …

Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance

The utilization of artificial intelligence (AI) applications has experienced tremendous growth in recent years, bringing forth numerous benefits and conveniences. However, this expansion has also …

In Defence of Principlism in AI Ethics and Governance

The Selective Labels Problem

Evaluating whether machines improve on human performance is one of the central questions of machine learning. However, there are many domains where the data is …

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 …

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 …

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 …

Data augmentation for fairness-aware machine learning

Researchers and practitioners in the fairness community have highlighted the ethical and legal challenges of using biased datasets in data-driven systems, with algorithmic bias being …