Data augmentation for fairness-aware machine learning: Preventing algorithmic bias in law enforcement systems

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 a major concern. Despite the rapidly growing body of literature on fairness in algorithmic decision-making, there remains a paucity of fairness scholarship on machine learning algorithms for the real-time detection […]

COPKIT: Technology and Knowledge for Early Warning/Early Action-Led Policing in Fighting Organised Crime and Terrorism

Intelligence-led policing methods and supporting analysis tools represent the state-of-the-art approach in analysing, investigating, mitigating and preventing crime. This chapter examines the question of how such methods and tools can address the lack of interaction between long-term high-level strategic intelligence and operational intelligence in the context of the fight against organised crime and terrorism. First, […]

Artificial intelligence for human flourishing–Beyond principles for machine learning

The technical and economic benefits of artificial intelligence (AI) are counterbalanced by legal, social and ethical issues. It is challenging to conceptually capture and empirically measure both benefits and downsides. We therefore provide an account of the findings and implications of a multi-dimensional study of AI, comprising 10 case studies, five scenarios, an ethical impact […]

Ethics and Privacy in AI and Big Data

Emerging combinations of artificial intelligence, big data, and the applications these enable are receiving significant attention concerning privacy and other ethical issues. We need a way to comprehensively understand these issues and find mechanisms of addressing them that involve stakeholders, including civil society, to ensure that these technologies’ benefits outweigh their disadvantages.