Bibliografias temáticas / AI control safety

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Literatura científica selecionada sobre o tema "AI control safety"

Autor: Grafiati

Publicado: 7 de junho de 2025

Última modificação: 2 de agosto de 2025

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Artigos de revistas sobre o assunto "AI control safety"

Abu, N. S., W. M. Bukhari, M. H. Adli, Hari Maghfiroh, and Alfian Ma’arif. "Advancements, Challenges and Safety Implications of AI in Autonomous Vehicles: A Comparative Analysis of Urban vs. Highway Environments." Journal of Robotics and Control (JRC) 5, no. 3 (2024): 613–35. https://doi.org/10.18196/jrc.v5i3.21114.

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This research reviews AI integration in AVs, evaluating its effectiveness in urban and highway settings. Analyzing over 161 studies, it explores advancements like machine learning perception, sensor technology, V2X communication, and adaptive cruise control. It also examines challenges like traffic congestion, pedestrian and cyclist safety, regulations, and technology limitations. Safety considerations include human-AI interaction, cybersecurity, and liability/ethics. The study contributes valuable insights into the latest developments and challenges of AI in AVs, specifically in urban and hig

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Jonsson, Roberth. "Avoiding AI Accidents." Industrial Vehicle Technology International 28, no. 1 (2020): 10. http://dx.doi.org/10.12968/s1471-115x(23)70471-6.

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Sharma,, Siddharth. "Revolutionizing Sports Bikes with Artificial Intelligence: Safety, Performance, and Design Innovations." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30618.

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This study examines how artificial intelligence (AI) is incorporated into sports bikes and examines the significant impacts this has on ride quality, user experience, and safety. Adaptive cruise control and collision avoidance systems, two AI-driven technologies that improve rider safety, and engine performance improvements and predictive maintenance algorithms that improve overall bike performance and dependability. Furthermore, bike design processes are revolutionized by AI-driven design techniques, which allow for quick iterations and customisation. This study offers insights into how artif

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Krainiuk, O., Yu Buts, N. Didenko, V. Barbashyn, and O. Trishyna. "METROLOGICAL CONTROL OF SENSORS FOR MONITORING WORKING CONDITIONS USING ARTIFICIAL INTELLIGENCE." Municipal economy of cities 3, no. 184 (2024): 216–22. http://dx.doi.org/10.33042/2522-1809-2024-3-184-216-222.

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Metrological control plays a vital role in ensuring the accuracy and reliability of the data collected as part of the working conditions monitoring. It helps to prevent potential errors and guarantee the quality of the results, which is critical for the efficient assessment and management of occupational health and safety. The article aims to investigate and analyse the role and importance of metrological control of sensors in the system for monitoring working conditions at production facilities using artificial intelligence. The article examines the possibilities of using artificial intellige

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Duarte, Ana Beatriz. "THE CASE FOR CONVERGENCE IN AI SAFETY." IA Policy Brief Series 11, no. 1 (2024): 1–8. https://doi.org/10.5281/zenodo.15073811.

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This policy brief emphasizes the need for a unified, evidence-based approach to AI safety. Given the inability to fully explain AI decisions, the global community has a growing consensus about the need to prioritize AI safety. However, discussions about AI safety often focus on different topics. While some scholars are alert to the dangers of cyberattacks and even the possibility of AI taking over, civil society experts claim for now-and-here issues such as the military use of autonomous weapons and other present-day risks. Common to the discourse of both groups is the concern with control, di

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Kamala Aliyeva, Rashid Jafarov, Kamala Aliyeva, Rashid Jafarov. "SAFETY CONTROL SYSTEMS FOR ETHYLENE PRODUCTION." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 35, no. 12 (2023): 206–13. http://dx.doi.org/10.36962/pahtei35122023-206.

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Ethylene production is a cornerstone of the petrochemical industry, with a global demand that continues to rise. Ensuring efficient, safe, and environmentally responsible ethylene production processes is paramount. Ethylene production control systems, encompassing a range of technologies and strategies, play a pivotal role in achieving these objectives. This abstract provides a concise overview of key aspects of ethylene production control systems, their challenges, and their critical importance. Ethylene production control systems serve as the backbone of modern ethylene plants, orchestrating

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Ourzik, Victoria Yousra. "Security and safety concerns in the age of AI." International Conference on AI Research 4, no. 1 (2024): 329–37. https://doi.org/10.34190/icair.4.1.3142.

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Artificial Intelligence (AI) is transforming industries at an astonishing rate, reshaping how we live, work, and interact with technology. Yet, as AI becomes more pervasive, it brings urgent questions about security and safety. This article explores these critical issues, drawing a clear distinction between AI security and AI safety—two concepts that are often misunderstood but are crucial for responsible AI deployment. AI security focuses on protecting systems from external threats like data breaches, adversarial attacks, and unauthorized access. As AI systems increasingly handle sensitive da

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Vishal Goyal, Kamal Sharma, Amit Jain,. "A Comprehensive Analysis of AI/ML-enabled Predictive Maintenance Modelling for Advanced Driver-Assistance Systems." Journal of Electrical Systems 20, no. 4s (2024): 486–507. http://dx.doi.org/10.52783/jes.2060.

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Advanced Driver-Assistance Systems (ADAS) are changing driver-vehicle interactions to improve road safety and reduce distractions. Technological advances like ADAS and AI in cars present societal challenges and opportunities. It shows how AI aids human-machine communication by improving motor skills. The auto industry is interested in ADAS because it can increase energy efficiency, safety, and comfort. Numerous studies have shown its benefits. ADAS and vehicle networking show promise, but establishing a sound control system is challenging. Model Predictive Control (MPC) is one answer to these

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Schöning, Julius, and Hans-Jürgen Pfisterer. "Safe and Trustful AI for Closed-Loop Control Systems." Electronics 12, no. 16 (2023): 3489. http://dx.doi.org/10.3390/electronics12163489.

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In modern times, closed-loop control systems (CLCSs) play a prominent role in a wide application range, from production machinery via automated vehicles to robots. CLCSs actively manipulate the actual values of a process to match predetermined setpoints, typically in real time and with remarkable precision. However, the development, modeling, tuning, and optimization of CLCSs barely exploit the potential of artificial intelligence (AI). This paper explores novel opportunities and research directions in CLCS engineering, presenting potential designs and methodologies incorporating AI. Combining

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Turchin, Alexey, David Denkenberger, and Brian Green. "Global Solutions vs. Local Solutions for the AI Safety Problem." Big Data and Cognitive Computing 3, no. 1 (2019): 16. http://dx.doi.org/10.3390/bdcc3010016.

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There are two types of artificial general intelligence (AGI) safety solutions: global and local. Most previously suggested solutions are local: they explain how to align or “box” a specific AI (Artificial Intelligence), but do not explain how to prevent the creation of dangerous AI in other places. Global solutions are those that ensure any AI on Earth is not dangerous. The number of suggested global solutions is much smaller than the number of proposed local solutions. Global solutions can be divided into four groups: 1. No AI: AGI technology is banned or its use is otherwise prevented; 2. On

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Mais fontes

Teses / dissertações sobre o assunto "AI control safety"

Yürek, Markus. "GATA-Assistenten – En konceptvalidering av röststyrning i GATA." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36453.

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GATA is a web-application used by truckdrivers transporting timber from assigned pick-up places in the forest to specific receiving locations. GATA is used to facilitate and ease the navigation, communication and planning behind each delivery. This create situations where GATA needs to be used while driving, this is both dangerous and illegal. To solve this problem a voice-controlled proof-of-concept application for the most common features used during driving was developed and given the name GATA-Assistenten. The application was designed with a concept of having a few basic features built upo

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Livros sobre o assunto "AI control safety"

Human Compatible: AI and the Problem of Control. Penguin Books, Limited, 2019.

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Human Compatible: AI and the Problem of Control. Penguin Books, Limited, 2019.

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Human Compatible: AI and the Problem of Control. Penguin Books, Limited, 2020.

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Capítulos de livros sobre o assunto "AI control safety"

Yazdi, Mohammad, Sidum Adumene, Daniel Tamunodukobipi, Abbas Mamudu, and Elham Goleiji. "Virtual Safety Engineer: From Hazard Identification to Risk Control in the Age of AI." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82934-5_5.

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Gruteser, Jan, David Geleßus, Michael Leuschel, Jan Roßbach, and Fabian Vu. "A Formal Model of Train Control with AI-Based Obstacle Detection." In Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43366-5_8.

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Roy, Madhu Bala, and Abhishek Roy. "Road Safety Using AI: Safety Index Formulation of Roads by Integrated Road and Road Traffic Violation Control System." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8695-4_6.

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Bensalem, Saddek, Panagiotis Katsaros, Dejan Ničković, et al. "Continuous Engineering for Trustworthy Learning-Enabled Autonomous Systems." In Bridging the Gap Between AI and Reality. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46002-9_15.

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AbstractLearning-enabled autonomous systems (LEAS) use machine learning (ML) components for essential functions of autonomous operation, such as perception and control. LEAS are often safety-critical. The development and integration of trustworthy ML components present new challenges that extend beyond the boundaries of system’s design to the system’s operation in its real environment. This paper introduces the methodology and tools developed within the frame of the FOCETA European project towards the continuous engineering of trustworthy LEAS. Continuous engineering includes iterations betwee

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Van Ments, Laila, Jan Treur, Jan Klein, and Peter H. M. P. Roelofsma. "Developing a Safety and Security AI Coach: A Second-Order Adaptive Network Model of Shared Mental Models in Hospital Teamwork." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-72075-8_2.

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Mokadem, Nisrine, Fakhra Jabeen, Jan Treur, H. Rob Taal, and Peter H. M. P. Roelofsma. "Increasing Safety and Security Through Cyberspace by an Adaptive Network Model for AI-Assisted Risk Management of Neonatal Respiratory Distress." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-72075-8_5.

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Praveen, P. Blessington, and K. Sivanantham. "Ensuring Safety and Security in Control Area Network-Based Automotive Embedded Systems with Advanced Encryption Standard Method Using Cloud Technology." In The Intersection of 6G, AI/Machine Learning, and Embedded Systems. CRC Press, 2025. https://doi.org/10.1201/9781003540212-16.

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Yi, Fengchao, Gang Guo, and Guoxia Liu. "Research on Safety Management and Control of Power Enterprises Based on AI and Edge Computing Technology." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1439-5_9.

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Myklebust, Thor, Tor Stålhane, and Dorthea Mathilde Kristin Vatn. "Level of Automation and Autonomy." In SpringerBriefs in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80504-2_17.

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Abstract Understanding the distinction between automation and autonomy is crucial as these two levels of system control shape how AI approaches, risk assessments, and requirements are structured. Automated systems operate on predefined instructions, performing tasks within set boundaries, while autonomous systems dynamically adapt and learn, evolving with their environments. This chapter explores these differences, along with the standards, regulations, and safety considerations that govern both automated and autonomous systems across various applications.

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Weigl, Linn-Marie, Fakhra Jabeen, Jan Treur, H. Rob Taal, and Peter H. M. P. Roelofsma. "Learning for a Better Safety and Security Culture Within an Organization: Reducing the Risk in Communication with AI Coaching for Security Communication Through Cyberspace." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-72075-8_8.

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Trabalhos de conferências sobre o assunto "AI control safety"

S, Dhananjeyan, Ruby Angel T. G, and Naveen Kumar S. "Her Shield: Women Safety System using Voice AI." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10984972.

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Anoba, Richard. "Potential AI Applications for Multi-Hazard PRA Model Configuration Control." In 19th International Conference on Probabilistic Safety Assessment and Analysis (PSA 2025). American Nuclear Society, 2025. https://doi.org/10.13182/xyz-46477.

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Hemalatha, S., S. Malatthi Sivasundaram, P. Nithya, S. Deepak, V. S. Udhayaragavan, and S. Yogesh Kumar. "AI-Powered Workplace Safety: Helmet and Face Detection using YOLO for Access Control." In 2025 5th International Conference on Pervasive Computing and Social Networking (ICPCSN). IEEE, 2025. https://doi.org/10.1109/icpcsn65854.2025.11035190.

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M, Shanmughapriya, V. Brindha Devi, Sundar Sriram S, Molesh Kumar S, and Nishanth S. "VeriDrone - A real time AI Enabled System to Monitor and Report Safety in Construction Environment." In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10780211.

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Pop, Mădălin-Dorin, Viorel Cărbune, and Viorica Sudacevschi. "Safety and Sustainability Indicators of Variable Speed Limit Control in Intelligent Driver Models." In 2024 IEEE 21st International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET). IEEE, 2024. https://doi.org/10.1109/honet63146.2024.10822951.

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Sharmila, K. Soni, and K. Ramesh Chandra. "Predicting Adverse Interactions: A Comprehensive Review of AI-Driven Drug-Drug Interaction Models for Enhanced Patient Safety." In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS). IEEE, 2024. https://doi.org/10.1109/icicnis64247.2024.10823221.

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Stefanakis, A. S., D. Kolokotsa, E. Kapartzianis, J. Bonis, and J. K. Kaldellis. "Novel PSE applications and knowledge transfer in joint industry - university energy-related postgraduate education." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.164061.

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The field of Process Systems Engineering (PSE) is undergoing a renaissance through the integration of artificial intelligence (AI) and machine learning (ML). This transformation is driven by the vast availability of industrial data and advanced computing power, enabling the practical application of sophisticated ML models. These models enhance PSE capabilities in design, control, optimization, and safety. The progress of ML and ever-present data collection address previously intractable problems, particularly in system integration and life-cycle modeling. ML-powered predictive algorithms are a

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Satone, Kalyani, Pranjali Ulhe, Chitra Dhawale, and Supriya Narad. "AI-powered wearables and devices for women’s safety." In INTERNATIONAL CONFERENCE ON INTELLIGENT TECHNOLOGIES FOR SUSTAINABLE ENERGY MANAGEMENT AND CONTROL 2023: ITSEMC2023. AIP Publishing, 2024. https://doi.org/10.1063/5.0244721.

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Paramasivam, M. E., Sutharshana Perumal, and Hariharan Pathmanaban. "Revolutionizing Road Safety: AI-Powered Road Defect Detection." In 2024 3rd International conference on Power Electronics and IoT Applications in Renewable Energy and its Control (PARC). IEEE, 2024. http://dx.doi.org/10.1109/parc59193.2024.10486759.

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Terra, Ahmad, Hassam Riaz, Klaus Raizer, Alberto Hata, and Rafia Inam. "Safety vs. Efficiency: AI-Based Risk Mitigation in Collaborative Robotics." In 2020 6th International Conference on Control, Automation and Robotics (ICCAR). IEEE, 2020. http://dx.doi.org/10.1109/iccar49639.2020.9108037.

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Relatórios de organizações sobre o assunto "AI control safety"

Mittelsteadt, Matthew. AI Verification: Mechanisms to Ensure AI Arms Control Compliance. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20190020.

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The rapid integration of artificial intelligence into military systems raises critical questions of ethics, design and safety. While many states and organizations have called for some form of “AI arms control,” few have discussed the technical details of verifying countries’ compliance with these regulations. This brief offers a starting point, defining the goals of “AI verification” and proposing several mechanisms to support arms inspections and continuous verification.

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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject

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Niles, Kenneth, Emily Leathers, Joe Tom, et al. Leveraging artificial intelligence and machine learning (AI/ML) for levee culvert Inspections in USACE Flood Control Systems (FCS). Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49210.

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Levee inspections are essential in preventing flooding within populated regions. Risk assessments of structures are performed to identify potential failure modes to maintain the safety and health of the structure. The data collection and defect coding parts of the inspection process can be labor-intensive and time-consuming. The integration of machine learning (ML) and artificial intelligence (AI) techniques may increase accuracy of assessments and reduce time and cost. To develop a foundation for a fully autonomous inspection process, this research investigates methods to gather information f

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Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance hu

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Hicks, Jacqueline. Export of Digital Surveillance Technologies From China to Developing Countries. Institute of Development Studies, 2022. http://dx.doi.org/10.19088/k4d.2022.123.

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There is evidence to show that Chinese companies, with some state credit backing, are selling digital surveillance technologies to developing countries, which are then sometimes used in authoritarian practices. However, there is little direct evidence to show that surveillance technologies sold by Chinese companies have more authoritarian potential than the technologies sold by non-Chinese companies. Some researchers define “surveillance technologies” as including any form of digital infrastructure. There is data to show that developing country governments are contracting Chinese companies to

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Dong, Jiqian, Runjia Du, Paul (Young Joun) Ha, Sikai Chen, and Samuel Labi. Development of AI-based and control-based systems for safe and efficient operations of connected and autonomous vehicles. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317571.

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