Hyper-surveillance

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Security cameras are used in hyper surveillance schemes. Security camera, September 2018.jpg
Security cameras are used in hyper surveillance schemes.

Hyper-surveillance is the intricate surveillance of an entire or a substantial fraction of a population in order to monitor that group of citizens that specifically utilizes technology and security breaches to access information. [1] As the reliance on the internet economy grows, smarter technology with higher surveillance concerns and snooping means workers to have increased surveillance at their workplace. [2] Hyper surveillance is highly targeted and intricate observation and monitoring among an individual, group of people, or faction. [3]

Contents

History

In the middle of the 1970s, the American penal system or prison system expanded rapidly. [4] As a result, 1 in 35 adults are in correctional supervision nationwide. The surveillance systems has created targeted and specific supervision. [5] The use of surveillance systems has been targeted against black and Latino men. Consequently, men of color are found to be stopped by police at higher rates.  For example, in some neighborhoods, police stop over 500 out of 1000 residents in their lifetime due to hyper surveillance systems. [6]

Hyper surveillance extends beyond the crime control agents and police system as it has been documented in schools, community organizations, and other places. Research finds that hyper surveillance can lead to targeted and specific focus on an individual leading to profiling and predictive policing. [7]

Technology

Facial recognition systems

Constructed through computer programs, facial recognition systems analyze images and biometrics of human faces for the purpose of identification. [8] Compared to other facial systems, recognition software has been used for surveillance and security. [9] Alongside public video cameras, they can be used in a passive structure. Therefore, facial recognition software can be used without the knowledge or consent of a person. [10]

Practically, this technology can be used in state centers, offices, and workplaces. State departments possess photographs of constituents and utilize this information as a resource alongside public surveillance tools to create a system of identification and tracking. [11]

Mobile tracking systems

Mobile phone tracking is a system and process that identifies the location of a mobile phone. [12] It specifically locates the phone through radio signals or through the internal built GPS system. The technology has been used for observing objects or people on the move through specific and ordered locational processing. [13]

Use

A consumer technology that has created hyper surveillance technology is Clearview AI. [14] Some advantages that Clearview AI possess include security and efficiency. Using the technology, law enforcement is able to detect shoplifters, sex traffickers, child abusers, or homicide cases. [15] The software allows enforcement to a database of over three billion pictures allowing police to identify suspects efficiently. [16] However, there are multiple potential negatives including personal abuse, racial bias, inaccurate results, and data security. With access to millions of images, law enforcement could abuse the technology to identify romantic partners or foreign governments can identify people of social status to blackmail. The availability of information makes it more difficult for individual security. [17] In addition, Clearview AI has been proven to mistaken or misidentify suspects before. [18] The use of facial recognition technologies have biases leading to misclassifications causing wrongful arrests. In fact, the company stated that the tool finds matches 75% of the time. [7] Therefore, there are situations where data is inaccurate. A major concern with Clearview AI and facial recognition systems is the data security. In the past, Clearview AI has been hacked and the client base list has been leaked. [12]

Related Research Articles

<span class="mw-page-title-main">Closed-circuit television</span> Use of video cameras to transmit a signal to a specific place on a limited set of monitors

Closed-circuit television (CCTV), also known as video surveillance, is the use of closed-circuit television cameras to transmit a signal to a specific place, on a limited set of monitors. It differs from broadcast television in that the signal is not openly transmitted, though it may employ point-to-point, point-to-multipoint (P2MP), or mesh wired or wireless links. Even though almost all video cameras fit this definition, the term is most often applied to those used for surveillance in areas that require additional security or ongoing monitoring.

<span class="mw-page-title-main">Surveillance</span> Monitoring something for the purposes of influencing, protecting, or suppressing it

Surveillance is the monitoring of behavior, many activities, or information for the purpose of information gathering, influencing, managing, or directing. This can include observation from a distance by means of electronic equipment, such as closed-circuit television (CCTV), or interception of electronically transmitted information like Internet traffic. Increasingly, governments may also obtain consumer data through the purchase of online information, effectively expanding surveillance capabilities through commercially available digital records. It can also include simple technical methods, such as human intelligence gathering and postal interception.

<span class="mw-page-title-main">Facial recognition system</span> Technology capable of matching a face from an image against a database of faces

A facial recognition system is a technology potentially capable of matching a human face from a digital image or a video frame against a database of faces. Such a system is typically employed to authenticate users through ID verification services, and works by pinpointing and measuring facial features from a given image.

<span class="mw-page-title-main">Face detection</span> Identification of human faces in images

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene.

<span class="mw-page-title-main">Evercookie</span> JavaScript application programming interface

Evercookie is a JavaScript application programming interface (API) that identifies and reproduces intentionally deleted cookies on the clients' browser storage. It was created by Samy Kamkar in 2010 to demonstrate the possible infiltration from the websites that use respawning. Websites that have adopted this mechanism can identify users even if they attempt to delete the previously stored cookies.

<span class="mw-page-title-main">Domain Awareness System</span> Surveillance system by the NYPD and Microsoft

The Domain Awareness System, the largest digital surveillance system in the world, is part of the Lower Manhattan Security Initiative in partnership between the New York Police Department and Microsoft to monitor New York City. It allows the NYPD to track surveillance targets and gain detailed information about them, and is overseen by the NYPD Counterterrorism Bureau.

<span class="mw-page-title-main">Mass surveillance in India</span>

Mass surveillance is the pervasive surveillance of an entire or a substantial fraction of a population. Mass surveillance in India includes Surveillance, Telephone tapping, Open-source intelligence, Lawful interception, and surveillance under Indian Telegraph Act, 1885.

<span class="mw-page-title-main">Mass surveillance in China</span>

Mass surveillance in the People's Republic of China (PRC) is the network of monitoring systems used by the Chinese central government to monitor Chinese citizens. It is primarily conducted through the government, although corporate surveillance in connection with the Chinese government has been reported to occur. China monitors its citizens through Internet surveillance, camera surveillance, and through other digital technologies. It has become increasingly widespread and grown in sophistication under General Secretary of the Chinese Communist Party (CCP) Xi Jinping's administration.

DeepFace is a deep learning facial recognition system created by a research group at Facebook. It identifies human faces in digital images. The program employs a nine-layer neural network with over 120 million connection weights and was trained on four million images uploaded by Facebook users. The Facebook Research team has stated that the DeepFace method reaches an accuracy of 97.35% ± 0.25% on Labeled Faces in the Wild (LFW) data set where human beings have 97.53%. This means that DeepFace is sometimes more successful than human beings. As a result of growing societal concerns Meta announced that it plans to shut down Facebook facial recognition system, deleting the face scan data of more than one billion users. This change will represent one of the largest shifts in facial recognition usage in the technology's history. Facebook planned to delete by December 2021 more than one billion facial recognition templates, which are digital scans of facial features. However, it did not plan to eliminate DeepFace which is the software that powers the facial recognition system. The company has also not ruled out incorporating facial recognition technology into future products, according to Meta spokesperson.

<span class="mw-page-title-main">Meredith Whittaker</span> American artificial intelligence research scientist

Meredith Whittaker is the president of the Signal Foundation and serves on its board of directors. She was formerly the Minderoo Research Professor at New York University (NYU), and the co-founder and faculty director of the AI Now Institute. She also served as a senior advisor on AI to Chair Lina Khan at the Federal Trade Commission. Whittaker was employed at Google for 13 years, where she founded Google's Open Research group and co-founded the M-Lab. In 2018, she was a core organizer of the Google Walkouts and resigned from the company in July 2019.

Airport privacy involves the right of personal privacy for passengers when it comes to screening procedures, surveillance, and personal data being stored at airports. This practice intertwines airport security measures and privacy specifically the advancement of security measures following the 9/11 attacks in the United States and other global terrorist attacks. Several terrorist attacks, such as 9/11, have led airports all over the world to look to the advancement of new technology such as body and baggage screening, detection dogs, facial recognition, and the use of biometrics in electronic passports. Amidst the introduction of new technology and security measures in airports and the growing rates of travelers there has been a rise of risk and concern in privacy.

<span class="mw-page-title-main">Police surveillance in New York City</span>

The New York City Police Department (NYPD) actively monitors public activity in New York City, New York, United States. Historically, surveillance has been used by the NYPD for a range of purposes, including against crime, counter-terrorism, and also for nefarious or controversial subjects such as monitoring political demonstrations, activities, and protests, and even entire ethnic and religious groups.

Clearview AI, Inc. is an American facial recognition company, providing software primarily to law enforcement and other government agencies. The company's algorithm matches faces to a database of more than 20 billion images collected from the Internet, including social media applications. Founded by Hoan Ton-That and Richard Schwartz, the company maintained a low profile until late 2019, until its usage by law enforcement was first reported.

Hoan Ton-That is an Australian entrepreneur. He is the co-founder and chief executive officer of Clearview AI, a United States-based technology company that creates facial recognition software.

Government by algorithm is an alternative form of government or social ordering where the usage of computer algorithms is applied to regulations, law enforcement, and generally any aspect of everyday life such as transportation or land registration. The term "government by algorithm" has appeared in academic literature as an alternative for "algorithmic governance" in 2013. A related term, algorithmic regulation, is defined as setting the standard, monitoring and modifying behaviour by means of computational algorithms – automation of judiciary is in its scope. In the context of blockchain, it is also known as blockchain governance.

Regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating artificial intelligence (AI). It is part of the broader regulation of algorithms. The regulatory and policy landscape for AI is an emerging issue in jurisdictions worldwide, including for international organizations without direct enforcement power like the IEEE or the OECD.

Automated decision-making (ADM) involves the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with varying degrees of human oversight or intervention. ADM involves large-scale data from a range of sources, such as databases, text, social media, sensors, images or speech, that is processed using various technologies including computer software, algorithms, machine learning, natural language processing, artificial intelligence, augmented intelligence and robotics. The increasing use of automated decision-making systems (ADMS) across a range of contexts presents many benefits and challenges to human society requiring consideration of the technical, legal, ethical, societal, educational, economic and health consequences.

<span class="mw-page-title-main">Predictive policing</span> Use of predictive analytics to direct policing

Predictive policing is the usage of mathematics, predictive analytics, and other analytical techniques in law enforcement to identify potential criminal activity. A report published by the RAND Corporation identified four general categories predictive policing methods fall into: methods for predicting crimes, methods for predicting offenders, methods for predicting perpetrators' identities, and methods for predicting victims of crime.

Mass surveillance in Iran looks into Iranian government surveillance of its citizens.

Kashmir Hill is an American technology author and journalist currently employed by the New York Times. Her book, Your Face Belongs to Us, explored facial recognition technologies

References

  1. ↑ Campos-Manzo, Ana Lilia; Flores, Marisol; Pérez, Denise; Halpert, Zoe; Zevallos, Kevin (2020-07-01). "Unjustified: Youth of Color Navigating Police Presence Across Sociospatial Environments". Race and Justice. 10 (3): 297–319. doi: 10.1177/2153368717741346 . ISSN   2153-3687. S2CID   158363063.
  2. ↑ Glover, Karen S. (2008-01-01), Deflem, Mathieu (ed.), "Citizenship, hyper-surveillance, and double-consciousness: Racial profiling as panoptic governance", Surveillance and Governance: Crime Control and Beyond, Sociology of Crime, Law and Deviance, vol. 10, Emerald Group Publishing Limited, pp. 241–256, doi:10.1016/S1521-6136(07)00211-4, ISBN   978-0-7623-1416-4 , retrieved 2020-12-09
  3. ↑ Gidaris, Constantine (2020-03-01). "The Carceral Airport". Public. 30 (60): 76–91. doi:10.1386/public_00007_7. S2CID   216160207.
  4. ↑ "UCLA professor explains hyper-surveillance of black males". The Arizona State Press. Retrieved 2020-12-09.
  5. ↑ Reed, Andrew (2019-10-24). "Reed '21: Preventing a new era of hyper-surveillance". Brown Daily Herald. Retrieved 2020-12-09.
  6. ↑ Jeffries, Fiona (2011-04-01). "Saying Something: The Location of Social Movements in the Surveillance Society". Social Movement Studies. 10 (2): 175–190. doi:10.1080/14742837.2011.562362. ISSN   1474-2837. S2CID   145635074.
  7. 1 2 Ohm, Paul (November 1, 2018). "The Many Revolutions of Carpenter". osf.io. Retrieved 2020-12-09.
  8. ↑ Odella, Francesca (2019). "Privacy Awareness and the Networking Generation". Censorship, Surveillance, and Privacy. pp. 1309–1332. doi:10.4018/978-1-5225-7113-1.ch063. hdl:11572/284120. ISBN   978-1-5225-7113-1 . Retrieved 2020-12-09.
  9. ↑ Pina, Pedro. "Electronic Surveillance, Privacy and Enforcement of Intellectual Property Rights : A Digital Panopticon?". doi:10.4018/978-1-61350-323-2.ch413 . Retrieved 2020-12-09.
  10. ↑ "Facial Recognition » Avigilon". www.avigilon.com. 2020-04-23. Retrieved 2020-12-09.
  11. ↑ "SSRN Electronic Library". papers.ssrn.com. SSRN   3273483 . Retrieved 2020-12-09.
  12. 1 2 Capers, I. Bennett (2019), Miller, Eric J.; Lave, Tamara Rice (eds.), "Citizenship Talk", The Cambridge Handbook of Policing in the United States, Cambridge Law Handbooks, Cambridge: Cambridge University Press, pp. 473–490, ISBN   978-1-108-42055-6 , retrieved 2020-12-09
  13. ↑ Wilcox, Walter M. (2019-08-23). "Quantum Particle Scattering". Quantum Principles & Particles. pp. 429–487. doi:10.1201/b22285-11. ISBN   9781138090439. S2CID   203041991 . Retrieved 2020-12-09.{{cite book}}: |website= ignored (help)
  14. ↑ Zhang, Y.; Li, M.; Dong, Z. Y.; Meng, K. (June 2019). "Probabilistic anomaly detection approach for data-driven wind turbine condition monitoring". CSEE Journal of Power and Energy Systems. 5 (2): 149–158. doi: 10.17775/CSEEJPES.2019.00010 . ISSN   2096-0042.
  15. ↑ Giroux, Henry A. (2015-03-04). "Totalitarian Paranoia in the Post-Orwellian Surveillance State". Cultural Studies. 29 (2): 108–140. doi: 10.1080/09502386.2014.917118 . ISSN   0950-2386. S2CID   143580193.
  16. ↑ Timan, Tjerk; Albrechtslund, Anders (2018-06-01). "Surveillance, Self and Smartphones: Tracking Practices in the Nightlife". Science and Engineering Ethics. 24 (3): 853–870. doi:10.1007/s11948-015-9691-8. ISSN   1471-5546. PMC   5972155 . PMID   26319296.
  17. ↑ Wittenberg, David (1970-01-01). "Seeing around the next corner". Science Fiction Film & Television. 12 (2): 195–223. doi:10.3828/sfftv.2019.12. S2CID   198622264 . Retrieved 2020-12-09.
  18. ↑ Peters, Najarian (2020-01-01). "The Right to Be and Become: Black Home-Educators as Child Privacy Protectors". Michigan Journal of Race and Law. 25 (1): 21–60. doi: 10.36643/mjrl.25.1.right . ISSN   1095-2721.
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