Sixty-five-year-old Michael Williams was released from jail last month after spending almost a year in jail on a murder charge. The key evidence against him wasn\u2019t eyewitnessed testimony or forensics, but an audio recording from ShotSpotter<\/strong>, the most popular acoustic gunshot detection technology in the United States.<\/p>\n The \u201cgunshot\u201d sound that pointed the finger at Williams was initially classified as a firework<\/em> by the AI. After the charges were dropped due to \u201cinsufficient evidence\u201d it was revealed that one of ShotSpotter\u2019s human \u201creviewers\u201d had changed the data to fit the crime, reclassifying the sound as a gunshot instead of a firework [1]. The case highlighted the dangers that the system poses to civil liberties and brings to question how much power we should give to AI “witnesses”, especially those that can easily be tampered with.<\/p>\n What is ShotSpotter?<\/strong><\/p>\n Shotspotter is a patented acoustic gunshot detection system of microphones, algorithms, and human reviewers that alerts police to potential gunfire [2]. Once an \u201cexplosive type sound\u201d [3] is detected, the sensors switch on and create a three-second audio recording. If three sensors capture the same sound, the recording is sent for further verification at ShotSpotters Incident Review Center. After noise filters remove sounds from construction, fireworks, and other gunshot-like sources, the potential gunshots are then sent to human reviewers\u2014who decide if the police should be alerted.<\/p>\n ShotSpotter\u2019s claim is that the system has a 97% accuracy rate is unsupported by any actual evidence.\u00a0But that isn\u2019t stopping cities from paying a subscription of between $65,000 and $90,000 per square mile per year to install the technology [4]: Chicago\u2019s three-year contract with ShotSpotter cost $33 million [5].<\/p>\n How Does The Algorithm Work?<\/strong><\/p>\n What\u2019s under the hood? No one outside of ShotSpotter knows; the \u201cdeep learning\u201d classifier at the heart of the gunshot detection system has not been independently assessed nor peer-reviewed. The company states they have \u201ctwo AI algorithms,\u201d one to determine the location of the gunfire and one to filter noise. The following “fact” from the ShotSpotter website provides a few hints [6]:<\/p>\n However, the algorithm that determines the location of the gunfire [7] is a simple triangulation algorithm that is definitely not<\/em> AI, despite the company\u2019s claims, highlighted above in yellow. The nuts and bolts of the second algorithm, the noise filter, is kept tightly under wraps. Cities using the systems are expressly forbidden from sharing the data with any outside sources\u2014even research institutions [8], which makes it impossible to validate the system\u2019s effectiveness or true positive rate. Although ShotSpotter has never been independently evaluated, it has been used as evidence in many court cases [1].<\/p>\n False Positive Rate May be as High as 90%<\/strong><\/p>\n Although we know nothing about the \u201cAI, we do know that the system\u2019s false positive rate is<\/em> somewhere between 33% to 90%, depending on who commissioned the report; the ACLU puts the rate at the higher end [9], while a report commissioned by ShotSpotter puts the figure at the lower end:<\/p>\n Instead of alerting police to actual gunfire, they were alerting police to \u201cdumpsters, trucks, motorcycles, helicopters, fireworks, construction, vehicles traveling over expansion plates on bridges or into potholes, trash pickup, church bells, and other loud, concussive sounds common to urban life\u201d [1].<\/p><\/blockquote>\n So even if the classifier is a novel AI, it isn\u2019t a particularly good one.\u00a0 A whopping 86% of \u201cgunshot\u201d reports to the police lead to no report of any crime at all [3]. Chicago\u2019s Office of Inspector general (OIG) concluded from its analysis that CPD responses to ShotSpotter alerts rarely produce documented evidence of a gun-related crime, investigatory stop, or recovery of a firearm [10].<\/p>\n Changing the System<\/strong><\/p>\n Considering that the system has multiples issues, including the high false positive rate and data can be easily changed to \u201cfit the crime,\u201d it may be time for cities to redirect their million-dollar budgets to more effective ways of preventing crime. While ShotSpotter does have the potential to identify gun violence hotspots within cities [1], the technology shouldn\u2019t be used in its current guise: an unreliable \u201canonymous tipster\u201d sending even more unwarranted police responses to minority neighborhoods that are already over-policed.<\/p>\n \u00a0<\/p>\n \u00a0<\/p>\n References<\/strong><\/p>\n Police Image: Adobe Creative Cloud (Licensed)<\/p>\n [1] ShotSpotter \u2013 The New Tool to Degrade What is Left of the Fourth Amendment<\/a><\/p>\n [2] ShotSpotter Precision Policing Platform<\/a><\/p>\n [3] Leaders Weigh Pros and Cons of ShotSpotter<\/a><\/p>\n [4] High Tech Ears Listen for Shots<\/a><\/p>\n [5] Chicago Police Department’s Use of ShotSpotterTechnology<\/a><\/p>\n [6] ShotSpotter responds to false claims<\/a><\/p>\n [7] Precision and accuracy of acoustic gunshot location in an urban environment<\/a><\/p>\n [8] 29. Jason, Tashea, Should The Public Have Access To Data Police Acquire Through Private Companies?, A.B.A J. 6 (Dec. 1, 2016).<\/p>\n [9] Four problems with the ShotSpotter gunshot detection system<\/a><\/p>\n [10] THE CHICAGO POLICE DEPARTMENT\u2019S USE OF SHOTSPOTTER TECHNOLOGY<\/a><\/span><\/p>\n \u00a0<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a><\/p>\n<\/p>\n
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