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Alan Turing Institute publishes framework for automated analytics in in security and law enforcement

31/05/23

Mark Say Managing Editor

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The Alan Turing Institute has published a framework for the use of automated analytics in security and law enforcement.

Its Centre for Emerging Technology and Security (CETaS) has produced the framework in the findings of a research report on the proportionality of the technology, Privacy Intrusion and National Security in the Age of AI.

It reflects concerns that while automated analytics – using computers to analyse data – can prove valuable in protecting the nation it can intrude into people’s personal lives.

The research was based on interviews and focus groups with stakeholders across the UK government, national security and law enforcement, and legal experts outside government.

The authors examined the obligations of national security and law enforcement agencies to keep citizens safe in a challenging operational environment, while minimising intrusiveness and adhering to the legal principle of proportionality in surveillance.

The subsequent framework involves six key factors on datasets, results, human inspection, tool design, data management and timeliness and resources.

Cumulative risk

Three recommendations are highlighted, the first being to better understand, map and monitor the cumulative intrusion risk of multiple automated systems that feed into each other over an extended period.

Second is that there should be specific consideration of the potential of developing automated systems which may incur a degree of intrusion in the short run but reduce it in the long term. This requires the development of a way of measure the potential future reduction in intrusion.

Third is the need for a more rigorous and representative understanding of shifting public expectations of privacy against the backdrop of big data analytics and automated systems.

Dr Marion Oswald, lawyer and senior research associate at the institute, as well as one of the report’s authors, said: “We need to better understand, map and monitor the risk of multiple, connected, automated systems feeding into each other over an extended period.

“We hope this framework will be adopted by people across the national security and law enforcement communities, such as analysts, investigators, legal advisers, oversight bodies and judicial commissioners.” 

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