Cognitive Engine Technology Can Help Public Safety Agencies

There Are Several Ways Public Safety Agencies Benefit From Employing Cognitive Engine Technology In RTCCs

Cognitive Engine Technology Can Help Public Safety Agencies
By Houston Thomas III

Public safety agencies across the country have been deploying next-generation work centers, which use modern technologies and data analysis tools to integrate multiple data streams into a single, cohesive picture. That gives public safety officials the ability to assess a situation in real time and make decisions accordingly.

Real-time crime centers (RTCC) are a prime example of the next-generation work center concept. In RTCCs, public safety agencies can use data visualization, analytics and artificial intelligence technologies to glean insights to legacy information as well as real time data feeds. This capability leads to better and faster decision-making.

A key enabling technology for this kind of analysis is a cognitive engine, which are AI applications that can process unstructured data from multiple sources so users — public safety agencies in this case — can quickly extract actionable intelligence. These cognitive engines can enable law enforcement agencies to more quickly cull and analyze body camera footage, edit rich media, create transcripts and more.

In the case of RTCC environments, analysts and detectives may have to access 15 to 20 different data sources in order to determine possible persons of interest in a given case. Cognitive engines reach out across those sources and consolidate the information into dashboards as well as mapping programs, enabling faster, more concise information from which to base a decision.

Outside of response activities, cognitive engines are also useful for law enforcement to enhance transparency with the public. Cognitive engines can assist agencies to more quickly release information about law enforcement interactions with citizens.

What Is a Cognitive Engine?

A cognitive engine is an AI application that ingests unstructured data and analyzes it, then produces a result in a structured form.

There are cognitive engines for a wide range of different functions. For example, in the realm of text analysis, there are engines for language identification, translation, content classification, summarization, sentiment analysis and more. For analyzing video, engines exist for object detection, license plate recognition and optical character recognition. For speech, engines can be used to convert speech in audio or video files into text transcripts, identify speakers based on recordings of their voices and break audio files down into segments that separate out different speakers.

One of the leading providers of AI engines is a company called Veritone, which has an operating system for AI, aiWARE. The software “orchestrates a diverse ecosystem of machine learning models to transform audio, video, text, and other data sources into actionable intelligence,” according to the company’s website.

Veritone notes the platform “uses machine learning across the AI engine ecosystem to orchestrate and employ the best engines for the job, always producing optimal results.”

In the public safety context, such a solution has a variety of useful applications. One is to enable law enforcement to more quickly release to the public footage from body cameras, dash cameras or surveillance cameras, while redacting sensitive information such as the identity of innocent individuals or information that would compromise an ongoing investigation.

Such a tool allows public safety agencies to more quickly respond to public calls for the release of footage of interactions with citizens. Instead of having users engage in a time-consuming manual redaction process, the software can detect human faces and lets users define other sensitive imagery, according to Veritone. The tool then automatically redacts it from audio-, video- and image-based evidence.

The Benefits of Cognitive Engines for Public Safety

There are several ways public safety agencies benefit from employing cognitive engine technology in RTCCs. One is obviously increased transparency. The more quickly evidence can be analyzed and redacted appropriately, the more quickly in can be released to the public.

Delays in releasing such evidence can sometimes lead to the perception that an agency is hiding something. Expediting the review and release of evidence can increase public trust and strengthen the relationship between a law enforcement agency and the community it serves.

“As law enforcement agencies are under increasing scrutiny, we welcome more transparency. We want to be able to share relevant videos proactively with the public, particularly those from body cams,” says David Jantas, the former chief of police at New Jersey’s Pemberton Township Police Department, on Veritone’s site. “To make this happen, we needed sophisticated redaction software. Veritone quickly emerged as the right partner –– its Redact solution checked all of our boxes for security, including CJIS compliance and the stability of Microsoft Azure’s cloud. It’s also saving us taxpayer dollars, resources and time.”

Cognitive engines are also force multipliers. They help cut down on the resources and time required of staff to analyze evidence. Instead of focusing on time-intensive but low-value tasks such as redaction and transcription, personnel can focus on higher-value work.

At a time when public safety agencies are facing greater demands for transparency from the public, technology tools can help them meet those demands while serving their communities.

This news was originally published at State Tech Magazine

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