It is a slow-rolling catastrophe that doesn’t show any sign of slowing soon. The AiLecs project is small and targeted but is among a growing number of machine learning algorithms law enforcement, NGOs, business and regulatory authorities are deploying to combat the spread of child sexual abuse material online. Monash University will retain ownership of the photograph database and will impose strict restrictions on access.
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“That’s where the triaging comes in says if you want to look for this stuff, look here first because the stuff that is likely bad is what you should be seeing first.” It will then be up to an investigator to review each image flagged by the algorithm. “A person gets caught and you think you’ll find a couple hundred pictures, but it turns out this guy is a massive hoarder and that’s when we’d spend days, weeks, months sorting through this stuff.” The algorithm will be used when a computer is seized from a person suspected of possessing child sexual abuse material to quickly point to where they are most likely to find images of children– an otherwise slow and labour-intensive process that Dalins encountered while working in digital forensics. Eventually a machine learning algorithm will be made to read this album again and again until it learns what a child looks like. Once uploaded with information identifying the age and person in the image, these will go into a database of other safe images. In its new My Pictures Matter campaign, people above 18 are being asked to share safe photos of themselves at different stages of their childhood.