David sits in the office, tapping the arm of the chair and staring through the window at the sports fields and the trees beyond.The head teacher begins. She explains that the advanced behaviour tracking (ABT) system has signalled that he is an immediate threat, so the school has had to call in the police.David glances at the two young officers sitting in the corner of the room, staring right at him. He looks back to the headteacher. “ABT says there’s an 81 per cent risk that you will commit a violent act in the next three days,” she continues, her hands shaking. “There is a 67 per cent chance that act will be fatal to another student. There is an 87 per cent risk you will also do harm to yourself. This crosses all our pre-approved thresholds for action within the ABT system.” David thinks back to the questions.
He tries to work out which one gave him away. He touches the patch on his arm, and tries to work out what bits of information he unwittingly gave up. He pictures the smart cameras dotted around the school, and wonders what they saw and heard.
He looks at his mum and pushes his bag further under the headteacher’s desk with his foot. He tries to work out whether they know already that inside the bag is a knife and that today he intended to use it.
This scenario probably seems unlikely to you. Pre-crime detection is the stuff of science fiction and even if the technology did exist to track behaviour and make predictions, the ethical and privacy concerns of such technology would surely mean it would never find its way into schools.
And yet the tech does exist and it is already in classrooms – not to the degree in the story above, but we are further down the road to giving algorithms the power of decision making than many teachers realise. This technology is advancing fast, with the research labs of tech firms and universities combining to create tools that promise to stop the school shooter, stop the knife attack, stop the sexual assault, even stop self-harm and bullying – all before it happens. But can those promises be relied upon? And should we permit the use of this technology in schools so we can find out?
AI predict a riot
Most people of a certain age associate pre-crime prediction technology with Steven Spielberg’s 2002 dystopian sci-fi thriller Minority Report, which is based on a short story by Philip K Dick. The film envisages a future in which criminals can be identified, arrested and sentenced by a specialist “pre-crime” police unit before they manage to commit murder. It all seemed very farfetched at the time, but less than 20 years on, that technology is no longer complete fiction.
The most high-profile example of pre-crime detection now in operation is PredPol, a piece of software used by a number of US police departments. The predictive policing system grew out of a research project between the Los Angeles Police Department (LAPD) and University of California, Los Angeles (UCLA), which set out to see if crime statistics could be used for more than just historical purposes. Working with mathematicians and behavioural scientists from UCLA and Santa Clara University, the team developed a machine learning algorithm, which was further refined with input from crime analysts and officers from LAPD and the Santa Cruz (California) Police Department. Its job is to “predict where and when specific crimes are most likely to occur”. By using this information, police departments “know” where better to deploy their resources, which should, in theory, help to reduce crime rates. It’s not too much of a stretch to imagine a similar system working in schools. Analogue versions of it are in place already, with senior leadership team members positioned at crucial locations based on a bit of cobbledtogether data, teacher experience and context. A tech solution to smooth and refine the process would likely be welcomed with open arms by teachers rather than questioned because it is about locations, not individuals – and because the action is already familiar.
If you doubt the above, then you are probably unaware that more controversial technology is already in use in schools, with tools seeking out individual rather than general behaviour patterns.
In China, facial recognition software has been used to monitor the concentration level of students and “smart uniforms” have been used to track a student’s location. Predictive technology is so prevalent in Chinese schools that Lei Chaozi, director of science and technology at China’s Ministry of Education, told the BBC that the government plans to “curb and regulate” its use.
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