About 700 eligible offenders will leave jails in England and Wales on 1 October while some 2,550 are due to be released by the end of the year, under the government's early prison release plan. So why are so many people being let out of jail earlier than expected and how have they been chosen? Why are prisoners being released early? Quite simply, prisons are almost full - and not for the first time. As of last Friday, there were 87,499 people in 120 jails across England and Wales - with around 1,500 available beds.

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Customers inside a B&M store, which is one of more than 100 businesses that will be using the technology. Customers inside a B&M store, which is one of more than 100 businesses that will be using the technology. Civil liberties groups say Facewatch system in stores such as Sainsbury's and B&M is'dangerous escalation' Fri 10 Jul 2026 06.19 EDTLast modified on Fri 10 Jul 2026 06.57 Facial recognition technology in shops will soon alert police in real time to the presence of serious offenders, with civil liberties groups warning of a "dangerous escalation" towards surveillance and criminalisation in the retail sector. Facewatch, a facial recognition system used by more than 100 businesses including Sainsbury's, B&M and Spar to monitor thieves, said it was launching a UK-first feature to "alert police instantly when the most serious offenders trigger a live facial recognition match".

Loud eaters and phones nearly spoiled my cinema trip - and it's not just me The cinema lights are low and you're cocooned in your seat, ready for the film to transport you to another world. But just as you settle in, you're jolted back to reality. Audience members around you are scrolling on their phones, talking and munching loudly. Cinemas do clearly ask everyone not to disturb those around them - through the use of adverts, announcements and signs - but is behaviour in getting worse? I experienced disruption a few weeks ago while watching Ryan Gosling's sci-fi movie, Project Hail Mary, at a cinema in London.

Child sex abuse is becoming increasingly complex to police and officers are arresting an average of 1,000 potential offenders each month, the National Crime Agency (NCA) says. It says an increasing reliance on online platforms and advances in technology, such as AI image creation, are exacerbating the problem, with algorithms and digital communities connecting offenders to share and promote child sex abuse material. According to the NCA, the number of arrests has roughly doubled in the past three years. Statistically, potential offenders are in every community and victims in every school, the NCA said. It added that police cannot address the issue alone and called on technology companies to do more.

HMP Wandsworth gets green light to use AI after team sent in to find'quick fixes' after spate of mistakes Artificial intelligence chatbots could be used to stop prisoners from being mistakenly released from jail, a justice minister told the House of Lords on Monday. James Timpson said HMP Wandsworth had been given the green light to use AI after a specialised team was sent in to find "some quick fixes". A double manhunt was launched last week after the incorrect release of a sex offender and a fraudster from the prison in south-west London. Release errors over the past fortnight have been seized upon by opposition MPs as evidence of the helplessness of ministers in the face of chaos within the criminal justice system. David Lammy, the justice secretary, is expected to address parliament about the number of missing prisoners when MPs return on Tuesday. It is understood that AI could be used to read and process paper documents; help staff cross-reference names to ensure that inmates are no longer hiding their past crimes behind aliases; merge different datasets; and calculate release dates and sentences.

Criminal justice administrative data contain only a limited amount of information about the committed offense. However, there is an unused source of extensive information in continental European courts' decisions: descriptions of criminal behaviors in verdicts by which offenders are found guilty. In this paper, we study the feasibility of extracting these descriptions from publicly available court decisions from Slovakia. We use two different approaches for retrieval: regular expressions and large language models (LLMs). Our baseline was a simple method employing regular expressions to identify typical words occurring before and after the description. The advanced regular expression approach further focused on "sparing" and its normalization (insertion of spaces between individual letters), typical for delineating the description. The LLM approach involved prompting the Gemini Flash 2.0 model to extract the descriptions using predefined instructions. Although the baseline identified descriptions in only 40.5% of verdicts, both methods significantly outperformed it, achieving 97% with advanced regular expressions and 98.75% with LLMs, and 99.5% when combined. Evaluation by law students showed that both advanced methods matched human annotations in about 90% of cases, compared to just 34.5% for the baseline. LLMs fully matched human-labeled descriptions in 91.75% of instances, and a combination of advanced regular expressions with LLMs reached 92%.

Federal police say they have identified 59 alleged offenders as being in these online networks and have made an unspecified number of arrests. Federal police say they have identified 59 alleged offenders as being in these online networks and have made an unspecified number of arrests. Australian federal police will develop an AI tool to decode gen Z and Alpha slang and emojis in an effort to crackdown on sadistic online exploitation and "crimefluencers". The AFP commissioner, Krissy Barrett, used a speech at the National Press Club on Wednesday to warn of the rise of online crime networks of young boys and men who are targeting vulnerable teen and preteen girls. The newly appointed chief outlined how the perpetrators, who are overwhelmingly from English-speaking backgrounds, were grooming victims and then forcing them to "perform serious acts of violence on themselves, their siblings, others or their pets".

Applying complex legal rules characterized by multiple, heterogeneously weighted criteria presents a fundamental challenge in judicial decision-making, often hindering the consistent realization of legislative intent. This challenge is particularly evident in the quantification of non-pecuniary damages in personal injury cases. This paper introduces Soppia, a structured prompting framework designed to assist legal professionals in navigating this complexity. By leveraging advanced AI, the system ensures a comprehensive and balanced analysis of all stipulated criteria, fulfilling the legislator's intent that compensation be determined through a holistic assessment of each case. Using the twelve criteria for non-pecuniary damages established in the Brazilian CLT (Art. 223-G) as a case study, we demonstrate how Soppia (System for Ordered Proportional and Pondered Intelligent Assessment) operationalizes nuanced legal commands into a practical, replicable, and transparent methodology. The framework enhances consistency and predictability while providing a versatile and explainable tool adaptable across multi-criteria legal contexts, bridging normative interpretation and computational reasoning toward auditable legal AI.