Summary
Welcome to the first chapter of our artificial intelligence (AI) for public safety blog series. In this blog, we will be covering:
- Why missing persons investigations need to improve traditional investigative methods
- How AI can help investigations by detecting biomarkers, geospatial analysis, and predictive analysis
- Ways that AI, including Veritone Track, helps combat human trafficking while balancing privacy concerns
- And more
Every year, thousands of individuals in the US go missing, and investigators face numerous challenges in finding them. Traditionally, investigators rely on interviews, forensic evidence, and other manual methods to locate missing persons. However, with the advancement of AI technology, new solutions have emerged that could greatly enhance the efficiency and effectiveness of missing persons investigations.
The potential of AI in solving missing person cases is immense, with the power to enhance traditional methods and improve outcomes, ultimately bringing closure to families and loved ones and pursuing perpetrators or other persons of interest.
By introducing AI into these types of cases, investigations’ traditional methods can be enhanced and supplemented by AI’s ability to swiftly analyze large amounts of data, recognize patterns, process biometric identification, and more.
The current state of missing persons investigations
Traditional methods for missing persons investigations involve physical search and rescue (SAR) operations, such as ground, aerial, and water-based searches. Investigators also rely on investigative techniques such as witness interviews, surveillance footage analysis, and evidence collection, to locate missing individuals.
These methods, while effective, have limitations such as time, resources, and human error, making it difficult to find missing persons in a timely manner. And, as most of us know, timeliness is paramount in these types of investigations.
There is a strong need for new solutions as the number of missing person cases continues to grow worldwide — fortunately, AI is revolutionizing public safety with each passing day.
Modernizing investigations with technology can address these limitations and help locate missing persons more efficiently. Advancements in AI can analyze large amounts of data, identify patterns, and process facial recognition and human-like objects (HLOs), improving the accuracy and speed of investigations.
How AI is transforming the search for missing persons
The search for missing persons is a complex and challenging task that has traditionally relied on manual investigation techniques. However, AI is transforming the search for missing persons by enhancing traditional methods with advanced algorithms, data analysis, and predictive modeling, especially in the areas of big data and predictive analysis, geospatial analysis, and biometric recognition.
Big data and predictive analytics
Big data and predictive analysis are also critical areas where AI is transforming search capabilities:
- Large datasets, including social media and public records, are now being used to predict probable locations and patterns of missing persons.
- Predictive modeling helps investigators narrow down search areas and focus resources where they are most likely to be effective.
- Natural language processing (NLP) is also used to analyze social media posts and gather valuable insights that can aid in the search for missing persons.
Geospatial analysis
Geographic information systems (GIS) are used to map and analyze terrain, providing critical information to search and rescue teams. AI automates processes and improves the accuracy of geospatial data analytics, enabling investigators to quickly analyze large amounts of data and identify patterns that traditional methods may inadvertently miss.
Biometric recognition
Compared to manual biometric recognition, AI-based biometric recognition algorithms offer greater accuracy and efficiency in identifying individuals beyond facial recognition technology.
For example, Veritone Track can use markers outside of personally identifiable information (PII) to define and track individuals and build timelines that can assist with identifying persons of interest, finding missing people, and preventing human trafficking.
AI in combating human trafficking
Human trafficking is a worldwide issue that often involves missing person cases. Because of its ability to identify victims and track perpetrators through pattern recognition, data analysis, and machine learning, AI is playing a crucial role in combating human trafficking.
AI algorithms can analyze large amounts of data from multiple sources (including CCTV, social media, and online platforms) to identify patterns and potential victims as well as build a timeline. This technology enables law enforcement to investigate and apprehend perpetrators, increasing public safety and potentially preventing future cases. Fortunately, this can all be done while maintaining privacy laws and protecting the PII of victims, perpetrators, and witnesses.
Balancing privacy concerns and public safety
As AI becomes more prevalent in law enforcement, balancing privacy concerns and public safety is a critical issue. While AI has the potential to enhance public safety, it can also lead to privacy violations and abuse of power.
Needless to say, it’s essential to establish ethical and legal frameworks to regulate AI usage and protect privacy rights. This includes developing legislative measures and guidelines to ensure transparency, accountability, and oversight of AI-based systems.
Additionally, implementing best practices such as data anonymization and security measures can mitigate the risks associated with AI. Overall, ensuring privacy is a crucial component of police reform and promoting public trust in law enforcement and justice agencies.
The role of AI in police reform and missing person cases
AI is transforming police reform and law enforcement practices by enabling improved resource allocation and decision-making. Data-driven insights and automation help police departments allocate resources more effectively, optimize patrol routes, and reduce response times. AI algorithms also aid in decision-making processes by providing real-time information, analyzing data patterns, and predicting potential outcomes.
These tools can help law enforcement to identify areas of improvement, allocate resources more effectively, and ultimately enhance public safety. By utilizing the correct AI-powered tools and integrating a strong framework for how AI should be used, law enforcement agencies (LEAs) and justice agencies can ensure greater accuracy and speed in active investigations, uncompromised confidentiality and PII for improved case handling, and increased public safety and prevention of future incidents.
Future directions and applications of AI in missing person investigations
The future of AI in public safety and missing person cases will likely involve collaboration between LEAs and tech companies. By working together, we can develop more effective and efficient AI-driven tools to enhance search and rescue operations, as well as other applicable use cases. One potential application is in addressing bullying and preventing disappearances through early identification and intervention strategies using AI-powered monitoring and analysis.
As technology advances, we can expect new AI-powered tools and techniques to emerge, such as more sophisticated biometric recognition and predictive modeling. For public safety agencies, having access to the right tools is paramount.
AI continues to offer new solutions and directions for missing persons investigations. With Veritone Track, public safety teams can identify and locate persons of interest across several separate video files without the need for using PII — enabling faster investigations that maintain privacy laws and protect individuals’ personal information.
To learn more about how Veritone’s AI-powered Track solution can revolutionize missing persons investigations and ensure a safer future, reach out to a team member who can help answer questions or schedule a free demo.
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