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AI Revolutionizing Domestic Violence Prevention

In this post:

  • According to a report of the NSO, the number of people seeking support services due to domestic abuse is increasing, with the majority of the victims being women.
  • When traditional risk assessment methods are compared with machine learning techniques, the latter is reported to have a higher accuracy in identifying the victims.
  • Besides AI-driven risk assessment, the technology provides a range of instruments to help victims and enable prompt assistance. 

 

Artificial intelligence (AI) is lightening the way to a revolution in the war on domestic violence, coming up with innovative methods to assist in prevention and response. The latest report from the National Statistics Office indicates that the number of victims resorting to support services for domestic abuse is continuously increasing, with most of these victims being women. 

This is attributable to a considerable number of law enactments and the establishment of well-built reform institutions. However, it is still a hard task to manage domestic abuse for our authorities, and that is always worth finding more modern as well as effective instruments.

Automation of the risk assessment using machine learning

Machine-learning techniques are found to have a much higher accuracy in determining which victims of domestic violence are in the most vulnerable situation when compared to traditional risk assessment methods. Specifically, out of all the police response calls about domestic abuse received, about 10% reach the authorities again within a year about another violent incident. One key role of the police service is to analyze the threat level at which the victims of domestic abuse are going to be targeted once again in order to protect victims and stop further violence. Generally, it is done by using a standardized questionnaire.

However, it is possible to enhance the predictability of the next assaults by more than 30%, increasing the likelihood of the police being able to stop serious injuries. The analysis of existing information, such as criminal records, calls to the police, or reported cases of violence, using the machine-learning systems is way more accurate than the usual standard questionnaires used by our police departments, shows. Those systems can be crucial in situations where the police may be too slow to intervene and respond to the incident of domestic abuse, and the cases take time to be handled.  

Real-time intervention and prediction

Currently, police officers who are on domestic abuse calls are trained to complete the DASH (Domestic Abuse, Stalking, Harassment, and Honour-Based Violence) form. DASH consists of a checklist of about 27 questions, which is subsequently used, along with any other relevant information, to guide an officer’s assessment of the case as standard, medium, or high risk. If a case is categorized as high-risk, it means that a securable incident could occur at any time, initiating a deployment of resources meant to protect the victim.

Since DASH data could be substituted with different erring information about the people involved, such as criminal convictions, incidents of violence, or the number of calls to the police concerning domestic abuse, the machine-learning system could get even more precise.

Through substituting standard interrogation methods with AI-driven interferences, agencies of law enforcement may be anticipating a reoccurrence of violence with greater accuracy. Through allocating the resources towards the high-risk cases, preventive measures can be taken to ensure victims’ safety and avoid future harm.

Empowering victims through technology

Besides AI-driven risk assessment, the technology provides a range of instruments to help victims and enable prompt assistance. The safety apps, SMS-based services, and wearable devices like SOS bracelets offer people the ways to seek help discretely and hence remain safe in time of trouble.

It is worth noting, technology and AI will allow us to support domestic abuse victims through many efficient and innovative methods. With the aid of technology, gaps in data, documentation, reporting, and policy have been filled and the victims have been provided with faster and more efficient tools. Several exemplary projects and solutions need a campaign that will make people aware of the solutions and help them scale nationally.

Moreover, Crowdsourcing is a tool to comprehend domestic violence and sexual harassment; it also provokes the making of policy and institutional change. #StopFemicides crowdsourcing sites not only bring to the attention the scope and gravity of the problem but they also provide the much-needed transparency to help bring about change by putting pressure on the government and policymakers to take action.

At the same time, different AI and NLP (natural language processing) tools can be created to detect domestic violence and online harassment patterns. Such AI-empowered tools can help the victims of domestic violence by identifying their patterns that allow the experts in the relevant domain, local authorities, and police services to predict potential domestic violence cases and to prevent or act timely.

Disclaimer. The information provided is not trading advice. Cryptopolitan.com holds no liability for any investments made based on the information provided on this page. We strongly recommend independent research and/or consultation with a qualified professional before making any investment decisions.

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