Ben Ha is the Solutions Architect Director for Veritone’s Government, Legal and Compliance division. Ben has over 15 years of experience within the software industry, serving primarily in a technical pre-sales role. Ben has been working with clients in the federal government and legal space for the last 4 years.
Veritone designs human-centered AI solutions. Veritone’s software and services empower individuals at most of the world’s largest and most recognizable brands to run more efficiently, speed up decision making and increase profitability.
How does Veritone’s iDEMS integrate with existing law enforcement systems, and what specific efficiencies does it introduce?
Law enforcement agencies’ (LEAs) existing systems typically have data from many alternative sources, like body-worn camera systems, video management systems and other cameras and devices. iDEMS allows LEAs to construct connections in those existing systems with an API or other integration pathways. It then virtualizes excessive of those systems, permitting law enforcement to maintain the master data where it’s within the source systems. Contained in the Veritone Investigate application, the user has access to a low-resolution proxy file they’ll leverage for viewing, sharing, searching, analyzing, etc. Because the information is in a single central location, it is less complicated for the user to undergo the investigative process without switching between siloed applications.
Veritone Investigate also allows the user to leverage AI cognition to investigate what’s contained in the content itself. In other words, LEAs can use AI to structure unstructured data, providing metadata information that makes finding things much easier. Most systems simply act as data storage and don’t contain information in regards to the words spoken or the faces or objects contained in the content. With Investigate and the iDEMS solution, AI is natively built-in and runs routinely upon ingestion, eliminating the necessity to manually watch or take heed to content to acquire context, accelerating the investigative process.
What are the technical requirements for law enforcement agencies to implement Veritone’s iDEMS?
LEAs don’t have to possess significant technical requirements to implement Veritone’s iDEMS – in reality, the answer will work with almost any sized LEA no matter what systems they do or do not need in place. Because Veritone has ingestion adapters that may connect with various APIs, the one thing the LEA will need is someone with access to those existing systems. Also, iDEMS is cloud-based, and the LEA will need a high-speed web connection and a contemporary web browser.
Are you able to provide more details on how Veritone Track differentiates from traditional facial recognition technologies when it comes to accuracy and efficiency?
Traditional facial recognition relies on visible facial expression (eyes, nose, mouth, etc.) to discover an individual of interest. The difficulty is that if the video doesn’t capture the person’s face, the technology cannot discover or track that individual. For instance, if the footage only captures someone’s back, the person’s face is roofed by a mask or hoodie, or the video doesn’t have an optimal angle of the face, the facial recognition won’t work.
Alternatively, Veritone Track treats potential individuals of interest as objects in a process generally known as human-like objects (HLOs). Through HLOs, Veritone Track can construct a singular “person print” of that individual based on visually distinguishing attributes. These visually distinguishable attributes could possibly be a hat, glasses, backpack or in the event that they are carrying something of their hand, even the colour contrast between their clothing and shoes. It also considers the person’s body type, e.g., arm length, stature, weight, etc.
After constructing that person print, Veritone Track incorporates good old-fashioned police work through a human-in-the-loop that reviews and verifies potential matches. Ultimately, this method is more accurate and efficient than traditional facial recognition technologies.
How does the usage of human-like objects (HLOs) in Veritone Track enhance the identification process in comparison with using facial recognition?
Leveraging HLOs enhances the identification process since it doesn’t require the LEA to have access to the identical variables as traditional facial recognition, i.e., a totally visible human face. Veritone Track is flexible in that it’s going to use whatever information is accessible whatever the quality of the footage, the resolution or the angle (high up on the ceiling or at eye level) of the camera. Despite the benefits of Veritone Track, it and facial recognition aren’t mutually exclusive – LEAs can use each technologies concurrently. For instance, LEAs could use Veritone Track to construct an individual print from large amounts of lower-quality video while running facial recognition on video samples of front-facing shots of a possible person of interest.
How does Veritone’s AI-powered system assist in speeding up investigations while maintaining high standards of evidence handling?
Veritone Investigate, Veritone Track, or all of Veritone’s public sector applications use AI to dramatically speed up manual processes for LEAs, reducing weeks or days’ price of labor into a couple of hours, which is increasingly critical amid ongoing staffing shortages. Despite this accelerated speed, Veritone maintains high standards of evidence handling by not totally trusting AI outputs. These solutions leave the ultimate say to the human investigator to review the ultimate results. Veritone’s technology also enables humans to evolve to high standards of evidence handling and chain of custody. Likewise, they’ve built-in audit trails, so the LEA can see how the investigator arrived on the outcome. Put simply, AI doesn’t replace humans – it merely enhances their capabilities.
AI in law enforcement raises concerns about wrongful persecution of minorities, especially with cities like Detroit, Michigan experiencing multiple wrongful arrests in lower than 1 yr. How does Veritone address these ethical challenges?
First, Veritone at all times uses guardrails and safety measures to reduce the potential for wrongful persecution. For example, Veritone Track doesn’t use biometric markers corresponding to facial expression to construct person prints but relies on clothing, body type, etc. Second, these tools never scrape the web, social media or huge databases like a Passport Agency to acquire data. When an LEA uses our solutions in an lively case or investigation, it may only compare uploaded photo or video evidence against a database of known offenders with arrest records. Within the case of what happened in Detroit, Michigan, law enforcement used an answer that grabbed data from across the web with no human investigator being “within the loop” to validate the outcomes, leading to wrongful persecution of innocent residents.
Are you able to elaborate on how Veritone’s AI ensures the accuracy of the leads generated?
Veritone’s AI generates potential leads that human investigators can pursue. While the AI provides the investigator with helpful findings and results, the person still makes the ultimate decision. Again, the Detroit, Michigan, case saw law enforcement trusting facial recognition alone to do the job. This blind trust was ultimately problematic as these models relied on data that resulted in demographically or racially associated biases.
Furthermore, the information Veritone chooses to coach its AI engines and models are representative of the content. Before training the information, Veritone will redact sensitive video and audio elements from sources like body-worn cameras, in-car video, CCTV footage, etc., or use publicly available non-sensitive data. Likewise, Veritone will validate results with customer feedback for continuous improvement.
How does Veritone handle the potential for AI to perpetuate existing biases inside law enforcement data?
Veritone uses a multiple-model approach that works with many alternative third-party providers to acquire a bigger perspective fairly than relying purely on one AI model. Specifically, this method allows Veritone to standardize inside a given category of AI cognition, corresponding to transcription, translation, facial recognition, object detection or text recognition. By leveraging the “wisdom of the group,” Veritone can run the identical content against multiple models inside the same category of AI cognition to assist guard against biases.
What steps are taken to make sure that Veritone’s AI applications don’t infringe on privacy rights?
There are two best practices Veritone’s AI applications follow to make sure they don’t infringe on privacy rights. One: the shopper’s data stays the shopper’s data in any respect times. They’ve the fitting to administer, delete or do whatever they need with their data. Although the shopper’s data runs in Veritone’s secure cloud-hosted environment, they preserve full ownership. Two: Veritone never uses the shopper’s data without their permission or consent. Specifically, Veritone doesn’t use the shopper’s data to retrain AI models. Security and privacy are of the utmost importance, and customers will only ever work with pre-trained models that use data redacted of all of its sensitive, biometric and personally identifiable information.
How does Veritone balance the necessity for rapid technological advancement with ethical considerations and societal impact?
When developing AI at a rapid pace, the tendency is to make use of as much data as possible and continually harvest it to enhance and grow. While such an approach does are likely to lead to accelerated maturity of the AI model, it opens up various ethical, privacy and societal concerns.
To that end, Veritone is at all times in search of the best-of-breed AI. Throughout the generative AI craze, Veritone had early access to technology from OpenAI and other partners. Nonetheless, as an alternative of pushing ahead and deploying latest solutions immediately, we asked, “How will our customers actually use AI inside a correct use case?” In other words, after examining the mission and pain points of LEAs, we determined the way to apply Generative AI in a responsible way that kept humans at the middle while allowing users to realize their goals and overcome challenges.
For instance, Veritone Investigate contains a private and network-isolated large language model that may summarize spoken conversations or content. If a body-worn camera captures an incident or an investigator interviews someone, Veritone Investigate can transcribe that content and routinely summarize it, which could be very helpful for detectives or investigators who need to offer a summary of a whole interview in a brief paragraph to the DA or prosecution. Nevertheless, the person still has the prospect to review the AI-generated output to make essential edits and changes before submission.
