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Privacy enhancing technologies for trustworthy use of data

Posted by: , Posted on: - Categories: Data, Data-sharing, Responsible innovation, Trust

The CDEI has been researching the role of privacy enhancing technologies (PETs) in enabling safe, private and trustworthy use of data. Privacy is a fundamental right. Organisations have an obligation to protect privacy, and must consider important legal, ethical, and …

Building on pandemic prompted data-driven innovation in local government

Posted by: , Posted on: - Categories: Covid-19, Data, Data-driven technology, Responsible innovation

Today the CDEI publishes its report on local government use of data during the COVID-19 pandemic.  The report draws together the findings of a forum discussion hosted last year with local authority representatives, and is supplemented by individual interviews and …

September release: COVID-19 repository

Posted by: , Posted on: - Categories: Artificial intelligence, Covid-19, Ethical innovation

Almost all (13 of 16) of this month’s entries were related to healthcare, with the majority of those specifically looking at use-cases in hospitals. Given that the UK faces an ongoing public health crisis and is entering a second-wave of coronavirus infections, it is not surprising that these use-cases are the most prevalent at this time. 

August release: COVID-19 repository 

Posted by: , Posted on: - Categories: Artificial intelligence, Covid-19, Ethical innovation

The number of brand new use-cases that we are seeing each month has seen a downturn since we began compiling the COVID-19 repository, although we are continuing to find further examples of the existing entries that we have been tracking, indicating that existing use-cases are being adopted more widely. 

Explainer: Case study: AI-driven testing for COVID-19

Posted by: , Posted on: - Categories: Artificial intelligence, Covid-19, Data-driven technology

In a project at Oxford University Hospitals NHS Foundation Trust (OUH), researchers have built an AI-driven test to screen for COVID-19, in the first hour of a patient arriving at an emergency department. The AI model - trained using laboratory bloods, blood gases, and observations recorded routinely during 115,000 presentations to Oxfordshire’s Emergency Departments - looks for a ‘biochemical and physiological signature’ of COVID-19.