It has been reported that the British Army has purchased thirty new drones, which can fly in all manner of bad weather. These will be used to spot targets on the battlefield or during anti-terrorism operations. If the Army found its Super Recognisers, they would be the best troops to verify the identification of a wanted terrorist or other human target. The best humans always ensure equipment can be used most effectively.
UK Report on Facial Recognition AGAIN notes need for trained human operators→
/A report has been published by the British Journal of Criminology entitled:
‘Assisted’ facial recognition and the reinvention of suspicion and discretion in digital policing
The most relevant section is shown below, but the full report is on the link. It shows how AFR (automated facial recognition) needs human intervention.
Human–computer interaction
The potential of AFR becomes realized when computer-generated matches are resolved through human activity. In most instances, this either involves an initial decision to disregard a match or, conversely, a suspect being engaged by street-based intervention teams tasked with conducting additional identity checks with AFR-matched individuals. In practice, this ‘second stage’ of activity was an arena of contestation and negotiation.
Human operators, therefore, constitute an essential component of the AFR process and play the primary role in adjudication. Two officers usually carried out this role. Many received formal or informal training prior to deployment, and some occupied non-operational roles, meaning AFR was a novel experience for them. Variances in operator capability were evident across both research sites and these disparities mirrored those encountered in other forms of biometric policework, such as that identified in DNA typing activities (Cole 2002).
Such considerations shaped the deference some officers gave to the algorithm and, conversely, why others were more sceptical of its performance. During one SWP (South Wales Police) deployment, one operator was visibly frustrated with a lack of correct alerts being generated in their van, while, on the same day, operators conducting surveillance elsewhere in the city centre had succeeded in locating and arresting multiple ‘persons of interest’. As a result, this operator became less trusting of alerts generated by the system. Despite habituation to the system, the technology thus reduced the sense of suspicion he experienced. Similarly, in London, once an AFR match had first been deemed incorrect by operators (on the third day observed), the overall rate of disconfirmed alerts increased slightly. Such incidences demonstrate the varied responses among human operators of AFR. However, while deference to suggestions generated by algorithmic decision-making was largely habitual—and with 26 of 42 computer-generated alerts considered suitably credible to intercept a matched individual in London—it is important to acknowledge the important role of some officers’ (techno)scepticism.
Roles and interactions between adjudicating officers undertaking this duty varied considerably. Sometimes, one operator would be looking for the person in the crowd while the other was describing them aloud from the image captured on the screen: operators reported using key facial features, such as eyes, nose, mouth, jawline and hairline to inform their decisions. While not relevant to a subject’s appearance, some officers also recruited background information (e.g. offence type) for their deliberations. At other times, contrasting approaches occurred within the same operational team. In London, any disagreements over whether to launch an intervention were always resolved in the affirmative, though this was not the case during SWP deployments.
Technical difficulties sometimes limited the role of AFR. During mobile deployments in central London, radios continually failed to work inside the AFR van. The corollary effect of these network fractures is illustrated by the following field note:
Fourth AFR match of the first Soho deployment (0.57 threshold). The officer adjudicating images attempts to radio a request to intercept a suspect. Responding to failures of both the radio and mobile tablets he lent out of the van and tried to radio again. When this failed, he took off after the suspect on foot. By this time the individual had crossed almost the length of Leicester Square. Limited adjudication time. Decision-making was near instant. With reflection significant differences were apparent between the probe and gallery images. The gallery image had moles on the suspects face, the probe image had none. While difficult to ascertain at first, most tellingly they had different colour eyes. A false positive (MPS, 17 December 2018, 14:22 pm).
Compensating for technical difficulties, therefore, not only limited AFR capability but also compressed the time available for discretionary adjudication. During South Wales’ initial deployments for the Champions League Final when the system was still being configured, it was slow and often produced ‘lag’. For example, 90 seconds elapsed between the camera timestamp and real alert time, ‘which was especially evident where a potential match was brought up by the system’ (field note, SWP, 31 May 2017). This relationship between different components of human-technical networks also reflects a critique among accounts of surveillance informed by assemblage theories rehearsed above: while surveillance practices involve intricate relationships between different forms of technology, they are not necessarily enhanced by such unions. Single points of failure inhibit the network and reduce overall surveillance possibilities.
https://academic.oup.com/bjc/article/61/2/325/5921789#.YDycnz3-efQ.twitter
More Controls on Automated Facial Recognition in the US→
/The State of Massachusetts has now introduced legislation requiring a judge’s authority before police officers can carry out automated facial recognition comparisons. Local officers are forbidden from carrying out the checks, which can only be carried out by State law enforcement agencies or the FBI. Washington State is also setting the pace in controlling computerised systems. See the article:
Feedback from February Course
/The February on-line course was attended by 34 students from four continents - Australia and Europe, together with North and South America.
The top scoring student was Daniel from England, he stated:
“Overall I found the course to be extremely in-depth for its 3 day turn around and provided excellent insight into the world of super recognition and its benefits whilst also teaching basic behavioural analysis, laws and report writing which are all vital in this particular field of surveillance. I found the all the talks from active super recognisers and experts within the field to be immensely useful and helped provide a real glimpse into the expected life and careers of being a super recogniser.”
Germán from Barcelona - “I found the course extremely valuable. Not only do you assess your potential as a Super Recogniser but you also learn all the legal things that are relevant to this job. Mike delivers a very good training course and participants also get to learn from other professionals, who are experts in their field. I particularly enjoyed the insight given by Kelly, who really brings alive what it is like to do such an interesting job. Everything was very nicely explained.”
Manuella from Brazil - “Taking part of the Course was a unique experience, once besides possibility of interaction with people from different places of the world and having similar skills, learning about super recognition was crucial. The speeches, which involved scientific and practical aspects regarding to Super-recognisers, were carried out brightly by qualified professionals, and that work in the field of super recognition in some way. It was worth getting involved in the Course and having discovered new things about this huge universe and full of possibilities.”
The next on-line courses are 25th-27th May and 7th-9th September. Association members are entitled to a discount.
Super Recognisers spread to Spain→
/Super Recognisers featured in an article in the best-selling Spanish newspaper “El Mundo”. If you wish to read the full article (the link has a paywall), email mike.neville@superrecognisersinternational.com for a PDF version.
https://www.elmundo.es/papel/historias/2021/02/22/602f97c0fdddff13788b45b4.html
Why Super Recognisers should check automated identifications→
/In the UK, no one can be arrested or searched due to a computerised facial recognition identification alone. According to the Surveillance Camera Commissioner’s Code of Practice there must be “human intervention” before such actions are taken and Super Recognisers are the ideal people to provide that. The false identification of suspects, particularly black men, in the US by automated systems shows why this rule should be adopted internationally.
Article by Dr Craig Donald→
/Association members who have attended a face-to-face course will recall the excellent input from Dr Donald on spotting suspicious behaviour. In this article, the South African behavioural expert discusses the use of CCTV to identify criminal conduct and how operators can read body language.
Cloaking Software to Defeat Automated Facial Recognition→
/A new software named Fawkes has been developed in the US to subtly alter images on social media, so they cannot be matched using artificial intelligence. Another good reason for law enforcement to use HUMAN Super Recognisers! See the full article here:
Cloak your photos with this AI privacy tool to fool facial recognition - The Verge
CCTV User Group - Webinar on Super Recognisers→
/The CCTV User Group (also known as the National Association of Surveillance Camera Managers) is hosting a webinar on Thursday 25th February and asking the question: Should CCTV control room employ Super Recognisers?
You can book a place using this link:
Failings with Automated Facial Recognition Highlighted→
/The attached article shows how automated or computerised facial recognition is NOT a panacea to spotting criminals. The article highlights the following issues:
13000 faces were scanned resulting in just ONE arrest
Facial recognition devices identified the wrong person seven out of eight times
Cameras failed to spot any suspects out of 4,600 faces in London last February
Campaigners say cameras are 'dangerously inaccurate and waste public money
Human Super Recognisers can make such systems more effective. BUT humans are also cheaper and more effective. AND their identifications can be used as EVIDENCE in court.