ShotSpotter CEO says technology can reduce gun violence, July 21

Before Toronto adopts ShotSpotter, a technology that can locate gunfire, the city should consider an issue of fairness related to its design. 

ShotSpotter works by comparing signals from its microphones to a library of gunshot profiles.  If a signal matches a profile, then ShotSpotter generates a “positive,” a gunshot report.  Otherwise, no report is generated. 

ShotSpotter can make two types of error: false positives, in which gunshots are reported in response to non-gunshots, e.g., backfires; and false negatives, in which actual gunshots are not recognized and reported. 

False positives tend to bring police to scenes where no gunshots occurred.  These incidents may spark hostile encounters as police arrive expecting to find shooters while community members may not welcome the extra and unwarranted presence of police. 

False negatives tend to disappoint members of the broader community.  After all, their concerns for public safety motivated the purchase of ShotSpotter in the first place, so it is irksome when gunshots go uninvestigated by police while the system is running. 

At a given level of accuracy, there is a trade-off between errors, such that the less of one kind the system makes, the more of the other kind it makes.  This trade-off puts the two social groups in conflict: The more one group minimizes the errors it dislikes, the more the other group faces the errors it deplores.  This raises the problem of fairness to the social groups involved. 

ShotSpotter is a private company, so its performance data is proprietary and not available for public scrutiny or study.  However, one informal study suggests the false positive rate is about 33 per cent, while false negative occurrence is “very rare.”  Such a result is what would be expected if the ratio of errors were set to favour the interests of public security, as noted above. 

If ShotSpotter is ultimately adopted by Toronto, its configuration should be set in conjunction with an open and unbiased process involving all parties with an interest in its operational effects.  That would mean inclusion of members of any communities surveilled by the system.  Furthermore, all data generated in its operation should be freely available for study to researchers and members of the public.