Traditional responses to public safety threats have historically fallen on law enforcement, however, these responses have failed to understand the complex nature of the threat and result in non-precision, "dragnet" policing techniques. Human trafficking is a prime example where police tactics, like “sting operations”, don’t target the actual threat, but rather are archaic methods of trying to gather insight into the problem. Human trafficking is not a threat that communities can “arrest their way out of” nor is it the sole responsibility of law enforcement to address. Response must be unbundled, and multiple stakeholders must be engaged to effectively address it. The current siloed approach creates a situation where leaders do not have the right information to effectively coordinate responses leading to misunderstanding of the problem, response initiatives based on bias, and ineffective, wasteful resource allocation. Relying on a primary police response often leads to adverse encounters – false or inappropriate incarcerations, unintended enforcement action, and unnecessary uses of force. AINA’s goal is to reduce, if not eliminate, these types of encounters by applying modern AI techniques to properly understand the data, and thus more appropriately unbundle the response to human trafficking, direct resources appropriately, and coordinate across multiple stakeholders.
