AI-powered public surveillance systems: why we (might) need them and how we want them

Fontes, Ana Catarina; Hohma, Ellen; Corrigan, Caitlin C.; Lütge, Christoph

doi:10.1016/j.techsoc.2022.102137

Cited by 42 publications

(21 citation statements)

References 35 publications

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“…The presence of surveillance cameras in public spaces and their potential for continuous monitoring can lead to debates and discussions about civil liberties and data protection. Striking the right balance between security and privacy remains an ongoing challenge in surveillance technologies [ 9 ].…”

Section: Reviewmentioning

confidence: 99%

Exploring the Impact of Security Technologies on Mental Health: A Comprehensive Review

Malik,

Acharya,

Humane

2024

Cureus

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This comprehensive review explores the intricate relationship between security technologies and mental health. Security technologies, including physical security, cybersecurity, and surveillance measures, are integral components of our modern world, designed to protect individuals, organizations, and society from various threats. While they are vital in enhancing safety, they also have profound implications for mental well-being. The review delves into the positive impacts of security technologies, including their capacity to enhance personal safety, reduce anxiety and fear, and instill a sense of security. However, it also reveals the negative consequences, such as privacy invasion, surveillance-related stress, paranoia, and ethical concerns, which can erode mental health. User perception and trust are central to understanding how individuals experience security technologies. The review emphasizes the importance of ethical guidelines, user education, and technological advancements in mitigating negative impacts. By embracing an ethical-by-design approach, empowering users, and promoting public awareness, a balanced equilibrium between security and mental health can be achieved. The conclusion highlights the significance of ongoing research and interdisciplinary collaboration to navigate this intricate relationship effectively. By prioritizing ethical considerations and fostering a dialogue that values security and individual well-being, we can ensure a safer and more mentally healthy future in our technologically interconnected world.

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Section: Reviewmentioning

confidence: 99%

Exploring the Impact of Security Technologies on Mental Health: A Comprehensive Review

Malik,

Acharya,

Humane

2024

Cureus

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“…Artificial Intelligence (AI) stands at the forefront of a technological revolution with the potential to revolutionize various industries (Daugherty & Wilson, 2018;Fontes et al, 2022;Ullah et al, 2024). One particularly impactful application of AI is its integration into facial recognition systems, especially in the context of software security (Smith, 2020).…”

Section: Literature Review System Architecturementioning

confidence: 99%

“…As we delve into the era of AI-driven security, this research recognizes the dynamic nature of AI, allowing for continuous improvement in facial recognition accuracy. This adaptability is critical in addressing the evolving landscape of security threats (Amiri et al, 2024;Fontes et al, 2022;Roozkhosh et al, 2023). The intersection of AI and facial recognition not only enhances security measures but also prompts a reevaluation of privacy considerations and ethical frameworks (Leslie, 2020).…”

Section: Literature Review System Architecturementioning

confidence: 99%

Integration of Artificial Intelligence in Facial Recognition Systems for Software Security

Santoso,

Safitri,

Samidi

2024

SinkrOn

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Facial recognition technology, a cornerstone in modern software security, has seen significant advancements through the integration of Artificial Intelligence (AI). This research focuses on enhancing facial recognition systems by incorporating sophisticated machine learning algorithms and deep neural networks. By doing so, the goal is to increase the accuracy and reliability of these systems in security applications. The study uses a variety of facial datasets to train AI models that are adept at extracting facial features and recognizing patterns. These models are subjected to rigorous testing to evaluate their performance in terms of identification accuracy, processing speed, and adaptability to different environmental conditions. One of the key challenges addressed in the research is the system's vulnerability to errors and potential misuse. Ethical considerations and privacy concerns are at the forefront of the study. The research highlights the importance of designing AI-based facial recognition systems that respect user privacy and are resistant to biases, thus fostering trust and acceptance among users. The results of the study show a marked improvement in system performance, demonstrating enhanced recognition accuracy and speed, while maintaining robustness across different conditions. By offering practical recommendations for the development of secure, ethical, and privacy-aware facial recognition systems, this research contributes valuable insights into the integration of AI in software security. It underscores the importance of continuous innovation and ethical responsibility in the deployment of facial recognition technologies, shaping the future landscape of technological security measures

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“…With the advancement of technology, additional forms of surveillance are continually being integrated into the toolkit of law enforcement agencies, particularly through electronic means [16]. While the primary motivation behind the development and use of these new technologies has been to prevent terrorist attacks, they also prove highly valuable in investigating crimes such as gambling, extortion, smuggling, bribery, loan sharking, drug trafficking, trafficking in stolen goods, and corruption of governmental authorities [17].…”

Section: Review Of Related Literaturementioning

confidence: 99%

Digitally improving UK police surveillance and incidence response using real-time crowd reporting app: Digipolice

Onyemachi Joshua Ndubuisi,

Gift Adene,

Belonwu Tochukwu Sunday

et al. 2024

Global J. Eng. Technol. Adv.

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Overtime, evolving technologies has made ways of monitoring and controlling crime and action in the United Kingdom (UK) become more challenging which needs optimization with the help of technologies. Also with the rate of crime across cities in the UK like mass shootings, murder and gang gun battles, the UK Police system within localities are overwhelmed or may not efficiently cope with crime alerts and incidences coming in at increasing frequencies. However looking in the area of real time crowd reporting, there is need to leverage technology to enhance public safety, improve incident response, and foster community engagement. This necessitated the aim of this study to develop a digital policing surveillance app for crowd reporting in the UK. By utilizing digital policing surveillance apps, the aim is to improve crowd management capabilities by enabling real-time reporting of incidents and observations from both the public and law enforcement personnel. This was positioned to facilitate better situational awareness, efficient resource allocation, and effective response strategies. To achieve the objectives, Python Programming Language 3.8 were deployed together with a Django Framework and a Microsoft Visual Studio Build Tools. In the DP system, the Model-View-Controller (MVC) pattern, which divides an application into three interconnected components—the model, the view, and the controller—was used for the architectural design of the system. For the database, Django's ORM makes it simple to support many databases, and in this project straightforward SQLite3 database were utilised which is ideal for small-footprint applications. Using WebSockets to establish real-time, bidirectional communication between web clients and servers, the user interface and intelligence board for the system were developed. A clean designed frontend design system with Bootstrap version 5—an open-source, reusable design framework created by Twitter—was selected for its easy user interface. The system was put through 50 concurrent load tests with actual users swarming the app since the DP app was subjected to hundreds of people complaining in real time. 2. The host computer had a Core i5 vPro CPU running at 4GHz and 4GB of RAM. The result shows that 50 users successfully generated an average of 29.7 requests per second. The system scaled up nicely to 50 users and reached its highest response time of 2600 milliseconds (2.6 seconds) in total. A dramatic rise in activity and a longer response time occurred after there were more than 50 users. The system could not operate at its peak efficiency with 100 users because response times were inconsistent and exceeded 7000 milliseconds (7 seconds) for each request. The effectiveness of the online deployment was evaluated with respectable results, and the system requirements also underwent successful testing. The appropriate investigation of ethical issues was done to meet requirements. DigiPolice has the potential to develop into a cutting-edge programme for real-time crowd surveillance and incident reporting. UK Police can employ it to as advancement to its “My Police” with as fast-track real-time and collaborative crowd reporting enhancements.

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AI-powered public surveillance systems: why we (might) need them and how we want them

Cited by 42 publications

References 35 publications

Exploring the Impact of Security Technologies on Mental Health: A Comprehensive Review

Exploring the Impact of Security Technologies on Mental Health: A Comprehensive Review

Integration of Artificial Intelligence in Facial Recognition Systems for Software Security

Digitally improving UK police surveillance and incidence response using real-time crowd reporting app: Digipolice

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