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Recently, the European Parliament called for a ban on police use of facial recognition. In the US, too, some cities have restricted police use of facial recognition. The first question that comes to mind is - why ban police from using technology that is allowed to private companies? Point of difference The key difference between the way police use facial recognition and the way commercial facial recognition products work is that: The police get a picture of a suspect from a crime scene and want to find out: "Who is the person in the picture?" That requires as wide a database as possible. Optimally - photos and identities of all the people in the world. Commercial facial recognition products such as those used by supermarkets, football stadiums, or casinos answer different questions: "Is the person in the picture on the employees' list? Is the person in the picture on a watch-list of known shoplifters?" To answer these questions doesn't require a broad database but rather a defined list of employees or a watch-list of specific people against whom there is an arrest warrant or a restraining order. Use of facial recognition AnyVision helps organisations leverage facial recognition ethically to identify known persons of interest "Facial Recognition Apps Should Be Provided to the Police with an Empty Database". This is exactly the subject of the open letter sent by AnyVision, to the British Biometrics and Surveillance Camera Commissioner, Prof. Fraser Sampson, titled: "Facial Recognition Apps Should Be Provided to the Police with an Empty Database". AnyVision recently raised $235M from Softbank and another leading VCs is a visual AI platform company that helps organisations across the globe leverage facial recognition ethically to identify known persons of interest, including shoplifters, felons, and security threats. Ethical use of facial recognition AnyVision CEO Avi Golan wrote, "The ethical use of facial recognition is a thorny one and requires a nuanced discussion. Part of that discussion has to explain how facial recognition works, but, just as important, the discussion must also involve how the technology is used by police departments and what checks and balances are built into their processes.” “We recommend building their watchlists from the ground up based on known felons, persons of interest, and missing persons. Some facial recognition solution providers have scrapped billions of photos and identities of people from social networks, usually without their consent." "Unfortunately, this method of facial recognition has justifiably angered privacy groups and data protection agencies around the globe and damaged the public trust in accuracy and reliability of facial recognition systems.” Preventing invasion of citizen’s privacy We believe an unjustified invasion of citizens' privacy can be prevented, false arrests can be reduced" “We believe that lists of suspects should be limited and justified. In this way, unjustified invasion of citizens' privacy can be prevented, false arrests can be reduced and public confidence in technology can be increased.” Golan added: "AnyVision is willing to share its industry insights and best practices from our vast research experience with leading global players, including name-brand retailers, global hospitality and entertainment companies, and law enforcement agencies from around the world.” Balancing public order and crime prevention “If the regulations set forth by Surveillance Camera Code of Practice are committed to the principles outlined above, then law enforcement agencies can strike the right balance between the need to maintain public order and prevent crime with the rights of every person to privacy and non-discrimination before the law." Recently Clearview AI CEO told Wired; the company has scraped 10 billion photos from the web - 3 times more than was previously known.
The past year has elevated consumer awareness about personal safety, from COVID-19 issues to social unrest, making safety top-of-mind and the need for personal safety solutions, even more prevalent. In addition, consumers spent more time at home, as schools closed, events were canceled and remote work increased. This prompted two major shifts that, in my opinion, most significantly opened the need for and raised the popularity of mobile safety solutions. Demand for grocery and food delivery apps surged Rise in use of app-based delivery services During the past 18 months, the use of app-based delivery services has skyrocketed During the past 18 months, the use of app-based delivery services has skyrocketed. With more use comes more interactions among strangers in homes and businesses, and while the majority of these moments are completely safe and convenient, incidents are happening, ranging from uncomfortable situations to physical assaults. And, with more delivery drivers on the road, there are going to be more accidents among gig-economy workers. Based on recent estimates, food and grocery delivery are expected to remain popular, even as we get back to normal life. High popularity of mobile security apps and wearables With more work shifting from stationary locations to working on-the-go, mobile security apps or wearables can be a lifeline in all sorts of situations. It’s important for these mobile safety products to be comprehensive, dynamic and designed to address the full range of people’s safety and security needs, from providing simple human reassurance to dispatching emergency help. Domestic violence cases increased According to the National Domestic Violence Hotline, due to COVID-19 lockdown restrictions, domestic violence rose as a result of many victims being stuck at home with their abusers, while sheltering in place and working from home. Mobile safety apps, such as ADT’s SoSecure U.S. Attorneys General and other state-elected officials have endorsed mobile safety apps, like SoSecure by ADT These situations necessitate the need for discreet ways for victims to call for help. U.S. Attorneys General and other state-elected officials have endorsed mobile safety apps, like SoSecure by ADT, as a tool to help victims of domestic abuse, safely call for help, without alerting their abuser. Over the past year, the mobile safety app market has seen tremendous innovation, including more user-friendly ways to make SOS calls. Today, within a single app, a person can summon help hands-free, by saying a secret phrase, by text or by swiping a button. Extension of mobile safety into wearable devices And, users can connect with people trained to help in unsettling situations over video, which can be an effective deterrent and provide video evidence. We’ve also seen the extension of mobile safety into wearable devices, in order to make these devices more discreet and usable. There will always be some safety risks in our lives. However, the good news is there’s no need to live in a constant state of fear. The easiest, most direct way to be prepared and ready to ‘fight back’ is by having a personal safety tool in your pocket, a mobile safety app that is there, when you need it most.
Today’s organisations face numerous diverse threats to their people, places and property, sometimes simultaneously. Security leaders now know all too well how a pandemic can cripple a company’s ability to produce goods and services, or force production facilities to shut down, disrupting business continuity. For example, a category three hurricane barreling towards the Gulf of Mexico could disable the supplier’s facilities, disrupt the supply chain and put unexpected pressure on an unprepared local power grid. Delivering timely critical information Tracking such risk is hard enough, but managing it is even more difficult. A swift response depends on delivering the right information to the right people, at the right time. And, it’s not as easy as it sounds. Indeed, 61 percent of large enterprises say critical information came too late for them, in order to mitigate the impact of a crisis, according to Aberdeen Research (Aberdeen Strategy & Research). These challenges are accelerating the hype around Artificial Intelligence (AI) These challenges are accelerating the hype around Artificial Intelligence (AI). The technology promises to help us discover new insights, predict the future and take over tasks that are now handled by humans. Maybe even cure cancer. Accelerating the hype around AI But is AI really living up to all this hype? Can it really help security professionals mitigate risk? After all, there’s a serious need for technology to provide fast answers to even faster-moving issues, given the proliferation of data and the speed at which chaos can impact operations. Risk managers face three major obstacles to ensuring business continuity and minimising disruptions. These include: Data fatigue - Simply put, there’s too much data for human analysts to process in a timely manner. By 2025, the infosphere is expected to produce millions of words per day. At that pace, you’d need an army of analysts to monitor, summarise and correlate the information to your impacted locations, before you can communicate instructions. It’s a herculean task, made even more difficult, when we consider that 30 percent of this global datasphere is expected to be consumed in real time, according to IDC. Relevance and impact - Monitoring the flood of information is simply the first hurdle. Understanding its impact is the second. When a heat dome is predicted to cover the entire U.S. Pacific Northwest, risk managers must understand the specifics. Will it be more or less hot near their facilities? Do they know what steps local utilities are taking to protect the power grid? Such questions can’t be answered by a single system. Communication - Once you know which facilities are impacted and what actions to take, you need to let your employees know. If the event is urgent, an active shooter or an earthquake, do you have a fast, effective way to reach these employees? It’s not as simple as broadcasting a company-wide alert. The real question is, do you have the ability to pinpoint the location of your employees and not just those working on various floor in the office, but also those who are working from home? How AI and ML cut through the noise Although Artificial Intelligence can help us automate simple tasks, such as alert us to breaking news, it requires several Machine Learning systems to deliver actionable risk intelligence. Machine Learning is a branch of AI that uses algorithms to find hidden insights in data, without being programmed where to look or what to conclude. More than 90 percent of risk intelligence problems use supervised learning, a Machine Learning approach defined by its use of labelled datasets. The benefit of supervised learning is that it layers several pre-vetted datasets, in order to deliver context-driven AI The benefit of supervised learning is that it layers several pre-vetted datasets, in order to deliver context-driven AI. Reading the sources, it can determine the category, time and location, and cluster this information into a single event. As a result, it can correlate verified events to the location of the people and assets, and notify in real time. It’s faster, more customised and more accurate than simple Artificial Intelligence, based on a single source of data. Real-world actionable risk intelligence How does this work in the real world? One telecommunications company uses AI and ML to protect a mobile workforce, dispersed across several regions. An AI-powered risk intelligence solution provides their decision makers with real-time visibility into the security of facilities, logistics and personnel movements. Machine Learning filters out the noise of irrelevant critical event data, allowing their security teams to focus only on information specific to a defined area of interest. As a result, they’re able to make informed, proactive decisions and rapidly alert employees who are on the move. Four must-have AI capabilities To gain real actionable risk intelligence, an AI solution should support four key capabilities: A focus on sourcing quality over quantity. There are tens of thousands of sources that provide information about emerging threats - news coverage, weather services, social media, FBI intelligence and so much more. Select feeds that are trusted, relevant and pertinent to your operations. Swift delivery of relevant intelligence. To reduce the mean-time-to-recovery (MTTR), risk managers need an accurate understanding of what’s happening. Consider the different contextual meanings of the phrases ‘a flood of people in the park’ and ‘the park is at risk due to a flood’. Machine Learning continuously increases the speed of data analysis and improves interpretation. Ability to cross-reference external events with internal data. As it scans different data sources, an AI engine can help you fine-tune your understanding of what’s happening and where. It will pick up contextual clues and map them to your facilities automatically, so you know immediately what your response should be. Ready-to-go communications. Long before a threat emerges, you can create and store distribution, and message templates, as well as test your critical communications system. Handling these tasks well in advance means you can launch an alert at a moment’s notice. The ability to minimise disruptions and ensure business continuity depends on speed, relevance and usability. AI and ML aren’t simply hype. Instead, they’re vital tools that make it possible for security professionals to cut through the noise faster and protect their people, places and property.
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