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This article presents a part of the ongoing Economic and Social Research Council (ESRC)-funded project “FloraGuard: Tackling the illegal trade in endangered plants” that relies on cross-disciplinary approaches to analyze online marketplaces for the illegal trade in endangered plants, and explores strategies to develop digital resources to assist law enforcement in countering and disrupting this criminal market. This contribution focuses on how the project brought together computer science, criminology, conservation science, and law enforcement expertise to create a tool for the automatic gathering of relevant online information to be used for research, intelligence, and investigative purposes. The article also discusses the ethical standards applied and proposes the concept of “artificial intelligence (AI) review” to provide a sociotechnical solution that builds trustworthiness in the AI approaches used for this type of cross-disciplinary information and communications technology (ICT)-enabled methodology.

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Abstract-Online services are becoming increasingly datacentric; they collect, process, analyze and anonymously disclose growing amounts of personal data. It is crucial that such systems are engineered in a privacy-aware manner in order to satisfy both the the privacy requirements of the user, and the legal privacy regulations that the system operates under. How can system developers be better supported to create privacy-aware systems and help them to understand and identify privacy risks? Model-Driven Engineering (MDE) offers a principled approach to engineer systems software. The capture of shared domain knowledge in models and corresponding tool support can increase the developers' understanding. In this paper, we argue for the application of MDE approaches to engineer privacyaware systems. We present a general purpose privacy model and methodology that can be used to analyse and identify privacy risks in systems that comprise both access control and data pseudonymization enforcement technologies. We evaluate this method using a case-study based approach and show how the model can be applied to engineer privacy-aware systems and privacy policies that reduce the risk of unintended disclosure.