Artificial intelligence could be used to help catch paedophiles operating on the dark web, the Home Office has announced.

The government has pledged to spend more money on the child abuse image database, which since 2014 has allowed police and other law enforcement agencies to search seized computers and other devices for indecent images of children quickly, against a record of 14m images, to help identify victims.

The investment will be used to trial aspects of AI including voice analysis and age estimation to see whether they would help track down child abusers.

Earlier this month, the chancellor, Sajid Javid, announced £30m would be set aside to tackle online child sexual exploitation, with the Home Office releasing more information on how this would be spent on Tuesday.

There has been debate over the use of machine learning algorithms, part of the broad field of AI, with the government’s Centre for Data Ethics and Innovation developing a code of practice for the trialling of the predictive analytical technology in policing.

Boris Johnson, the prime minister, has made a crackdown on crime the central plank of his domestic agenda. His hardline approach will increase police officer numbers, put up the use of stop and search and incarcerate more offenders, handing down longer sentences.

Law enforcement bodies have welcomed the greater resources but critics described the approach as cynical populist politics designed to sway voters before a possible general election.

Priti Patel, the home secretary, said “vile predators who prowl the internet abusing children are cowards who need to be caught and punished”, adding that the money would make sure “online paedophiles are no longer able to hide in the shadows preying on our society’s most vulnerable”.

National Crime Agency statistics showed 2.88m accounts were registered around the world on child sexual abuse sites on the dark web last year, with at least 5% believed to be in the UK.

The UK also plans to co-host a summit on child sexual abuse in Ethiopia in December to look at how leaders around the world can work together to tackle the crime.

A paper by the security thinktank Rusi, which focused on predictive crime mapping and individual risk assessment, found algorithms that are trained on police data may replicate – and in some cases amplify – the existing biases inherent in the dataset, such as over- or under-policing of certain communities.

The paper also highlights the risk of “automation bias”, whereby police officers become overreliant on the use of analytical tools, undermining their discretion and causing them to disregard other relevant factors.