From awareness to intervention: AI can intervene before gender-based violence escalates

Gender-based violence is not merely a technical problem, and no app can address its structural causes. However, the research shows that carefully designed AI systems can reduce reliance on victim action at moments of extreme vulnerability.

Gender-based violence remains a persistent global challenge, with women and girls disproportionately affected. Despite decades of legal reform, public campaigns, and institutional support systems, violence continues to occur in private spaces where victims have limited ability to call for help. New research shows that technology may offer a critical safety layer in moments when victims are unable to ask for help, addressing one of the most persistent failures of existing protection systems.

The study, titled A Smart App for the Prevention of Gender-Based Violence Using Artificial Intelligence, and published in the journal Electronics, introduces and evaluates an AI-powered mobile application designed to autonomously detect danger situations through speech analysis and send emergency alerts without requiring any user action.

Why existing safety measures fail victims in real-world situations

The study outlines a key problem: most existing safety mechanisms assume that victims are able to actively request assistance at the moment of danger.

Hand gestures recognized as silent calls for help rely on visibility and the presence of witnesses. Panic buttons and emergency apps require conscious activation, which may be impossible during sudden escalation, physical restraint, or psychological intimidation. Court-mandated electronic bracelets worn by aggressors are reactive rather than preventive and depend on prior reporting, which many victims avoid due to fear, dependence, or lack of trust in institutions.

The research argues that these gaps create a dangerous window during which violence can escalate unchecked. In many domestic violence cases, verbal aggression and threats precede physical assault. However, existing systems rarely intervene during this escalation phase. By the time authorities are alerted, harm may already have occurred.

The no pAIn app is designed specifically to address this failure point. Instead of requiring a victim to press a button or make a visible signal, the application listens continuously for speech patterns associated with fear, distress, or escalating conflict. When these cues are detected, the system transitions automatically into an alert state and sends emergency notifications with real-time location data to predefined contacts or assistance services.

This approach reframes violence prevention as an early detection problem rather than a last-resort response. By focusing on the moments when danger is building, rather than after violence has occurred, the system aims to reduce reliance on victim agency at the most critical time.

How the AI-powered app creates an invisible layer of protection

The study examines in detail how artificial intelligence can operate discreetly and continuously on standard smartphones and smartwatches. The no pAIn app functions as a virtual sentinel, running silently in the background once activated with the user’s consent.

Rather than developing a new speech recognition model, the system leverages mature, large-scale speech-to-text engines already embedded in modern mobile operating systems. These engines are trained on extensive multilingual datasets and optimized for real-world acoustic conditions. The app adds intelligence at the orchestration level, using a finite-state logic that interprets recognized speech and determines when a situation crosses from normal conversation into potential danger.

The application operates through three conceptual states. In the initial listening phase, it continuously analyzes incoming audio without recording or storing any data. When a predefined danger expression or user-configured keyword is detected, the system enters a sentinel mode, activating geolocation tracking and preparing emergency workflows. If further verbal cues confirm escalating risk, the app moves into alert mode and automatically dispatches help requests.

This design prioritizes privacy by ensuring that no audio recordings, transcripts, or personal identifiers are stored or transmitted. All speech processing is transient and occurs in real time. Only minimal data required for assistance, primarily location coordinates and alert metadata, are shared after explicit user consent during setup.

Discretion is a key safety feature. The app does not display visible alerts, sounds, or notifications that could alert an aggressor. On smartwatches, the system offers an additional layer of concealment, allowing the app to function even when a smartphone is not easily accessible.

Emergency notifications are delivered through multiple channels, including direct calls, text messages, and messaging platforms, with optional cloud-based redundancy to ensure delivery even under network constraints. This multi-path approach reduces the risk that a single point of failure will prevent help from arriving.

Testing results show speed, reliability, and low energy cost

To evaluate real-world feasibility, the study reports extensive scenario-based testing conducted in simulated domestic environments. These tests focused on whether the app could reliably detect danger phrases under typical household noise conditions and deliver alerts quickly enough to be useful in emergencies.

Results show that the system detects configured danger expressions with very high reliability in quiet and moderately noisy environments, including background television noise and normal conversation levels. Detection rates decline in extreme acoustic conditions, such as overlapping voices or whispered speech, highlighting inherent limitations of speech-based systems. However, the use of multi-word phrases and user-defined keywords significantly improves robustness compared to single-word triggers.

The end-to-end response time is one of the system’s most notable strengths. From the moment a danger phrase is spoken, the app typically delivers emergency notifications within two seconds. This rapid response window is critical in situations where delays can mean the difference between intervention and escalation.

Battery consumption is another major concern for any always-on safety application. The study reports that the app consumes approximately five percent of battery capacity over 24 hours of continuous background operation on a mid-range smartphone. This level of energy use is comparable to common messaging or music applications and suggests that long-term daily use is feasible without burdening users.

Importantly, the study does not claim that the app can prevent all forms of violence. Silent attacks, situations where victims are unable to speak, or cases where devices are confiscated remain outside its primary operating scope. To mitigate these limitations, the system includes optional manual and gesture-based activation methods, such as tapping the device, which can trigger alerts if speech detection fails.

The author note that the app should be understood as a complementary prevention tool rather than a replacement for institutional measures, legal protections, or social support services. Its value lies in adding an automated, low-friction safety layer that operates when other systems fail.

Implications for technology-driven violence prevention

The study raises broader questions about how AI should be deployed in sensitive social contexts. Gender-based violence is not merely a technical problem, and no app can address its structural causes. However, the research shows that carefully designed AI systems can reduce reliance on victim action at moments of extreme vulnerability.

By embedding prevention into devices that people already carry, the approach lowers barriers to adoption and avoids stigmatizing or visibly marking users as at risk. The emphasis on privacy-by-design also addresses common concerns about surveillance and data misuse, which often undermine trust in safety technologies. 

Future development should focus on expanding language support, improving detection of stressed and whispered speech, and integrating additional signals such as motion or physiological data to enhance robustness. Large-scale deployment would also require coordination with certified emergency services and alignment with legal and ethical standards.