AI Reducing Prisoner Reoffending Rates Case Study
Introduction
We cannot mention which government or which justice department we are working with, but we can say we are heavily involved in the revolutionary use of AI to drive down prisoner reoffending rates. The relationship between brain injury and reoffending rates among prisoners is a subject of increasing interest in the field of criminology and neurology. Studies show a higher prevalence of brain injuries among incarcerated individuals compared to the general population, which may influence their behaviour and propensity to reoffend. This case study explores the strategies, implementations, challenges, and outcomes in using AI to identify brain injured individuals for special support and treatment which research points out will dramatically reduce reoffending rates.
Background
In the face of a growing prison populations, it’s recognised by governments that they have to innovate and transform their methods to reduce reoffending rates. Brain injuries, particularly traumatic brain injuries (TBIs), can lead to cognitive, behavioural, and emotional impairments. These impairments can affect decision-making, impulse control, and social behaviour, potentially increasing the risk of engaging in criminal activities. AI is now seen as a key strategy to identify offenders with TBI’s and segment them out of the prison population to be given special therapy to help mitigate the effects of TBI’s and reduce reoffending rates. A truely win win situation for the authorities and the offenders.
Prevalence
- High Incidence in Prison Populations: Research indicates a significantly higher rate of TBIs among prisoners compared to the general population. A study by Williams et al. (2010) found that 60% of offenders reported experiencing a head injury.
- Underdiagnosis: TBIs and other brain injuries are often underdiagnosed and untreated in prison populations.
Impact on Reoffending
- Cognitive Impairments: Brain injuries can impair cognitive functions crucial for judgment, planning, and impulse control, potentially leading to higher reoffending rates.
- Emotional and Behavioural Issues: Injuries can lead to problems with emotional regulation and increased aggression, factors often linked to criminal behaviour.
- Substance Abuse: There is also a correlation between brain injury, substance abuse, and criminal behaviour. Substance abuse can exacerbate the effects of brain injuries, further increasing the risk of reoffending.
Recommendations for Addressing the Issue
- Improved Screening and Diagnosis: Implementing systematic screening for brain injuries among newly incarcerated individuals. This is where AI is now playing a role.
- Tailored Rehabilitation Programs: Developing rehabilitation programs that specifically address the cognitive and behavioural challenges posed by brain injuries.
- Training for Staff: Educating prison staff about the implications of brain injuries on behaviour and the best practices for management.
- Post-Release Support: Ensuring ongoing support and monitoring for individuals with brain injuries after their release to reduce the risk of reoffending.
Implementation
The chosen AI Solution was based on a proven machine learning approach using an AI Driven Avatar based decision tree AI model.
Key Features
Using charity provided training data the key features of this transformational AI system were
- AI Avatar used to interview /question and support users.
- Trained AI Decision Tree model able to identify with high 90% accuracy prisoners for induction into dedicated Brain Injury Offenders Programme.
Effectiveness
Evaluation of the effectiveness of the AI solution is still on-going but re-offending rates amongst identified and treated prisoners is already showing signs of significantly outperforming those compared to the general prison population. We will update this part of the study as and when more results are allowed to be published.
Challenges and Solutions
- Data Privacy and Security: Ensuring customer data privacy while implementing AI solutions are always a significant challenge. Stringent data security measures and compliance with GDPR were key to success. Significant PEN testing was carried out to make sure systems were protected against cyber threats.
- Skill Gap: Upskilling employees to work alongside AI is often critical. Training programs and workshops to bridge this gap are very necessary.
Conclusion
Use of AI-driven transformation in the justice system is really starting to gain traction. Just a look at the COMPAS Case Study one can see that AI is really helping the justice system to fine tune criminal tariffs and anti-reoffending programmes in order to minimise /optimise the size of prison populations without additional risks to society.