Vol. 3 No. 1 (2023): African Journal of Artificial Intelligence and Sustainable Development
Articles

Ethical Considerations in the Deployment of IoT Sensors for Autonomous Vehicle Monitoring

Dr. André Cardoso
Associate Professor of Computer Science, Federal University of Minas Gerais (UFMG), Brazil
Cover

Published 14-05-2023

How to Cite

[1]
Dr. André Cardoso, “Ethical Considerations in the Deployment of IoT Sensors for Autonomous Vehicle Monitoring”, African J. of Artificial Int. and Sust. Dev., vol. 3, no. 1, pp. 198–221, May 2023, Accessed: Sep. 19, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/81

Abstract

The development and deployment of IoT devices entails complex and multidimensional issues related to privacy, security, safety, ethics, legal, and environmental considerations. The resulting heterogeneity and complexity of IoT ecosystems pose a rich and potentially confusing set of interactions among the individual systems and users, with the consequent risk of emergence of collective purposes and mechanisms for policy intervention. In order to optimally harness the individual devices in a collective take-on task, a top-down approach would have to take into account any composite functions which can be derived and to provide incentives to the device owners. Second, to avoid any unsafe and/or unethical synergistic effects — which neither a traditional bottom-up nor the mentioned top-down approach can guarantee — flexible and language-oriented soft law policies should be established to govern the collective action-states and kinematic network witnessed, adaptively and dynamically, in an ‘emic’ way. The authors believe that endorsing amongst the various collective interacting systems some commonly accepted soft laws is a crucial step towards managing human-centred IoT collectives in the public domain and their responsible collective behaviour [1].

Downloads

Download data is not yet available.

References

  1. Pulimamidi, Rahul. "Emerging Technological Trends for Enhancing Healthcare Access in Remote Areas." Journal of Science & Technology 2.4 (2021): 53-62.
  2. Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.
  3. Ponnusamy, Sivakumar, and Dinesh Eswararaj. "Navigating the Modernization of Legacy Applications and Data: Effective Strategies and Best Practices." Asian Journal of Research in Computer Science 16.4 (2023): 239-256.
  4. Shahane, Vishal. "Investigating the Efficacy of Machine Learning Models for Automated Failure Detection and Root Cause Analysis in Cloud Service Infrastructure." African Journal of Artificial Intelligence and Sustainable Development2.2 (2022): 26-51.
  5. Muthusubramanian, Muthukrishnan, and Jawaharbabu Jeyaraman. "Data Engineering Innovations: Exploring the Intersection with Cloud Computing, Machine Learning, and AI." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 1.1 (2023): 76-84.
  6. Tillu, Ravish, Bhargav Kumar Konidena, and Vathsala Periyasamy. "Navigating Regulatory Complexity: Leveraging AI/ML for Accurate Reporting." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.2 (2023): 149-166.
  7. Sharma, Kapil Kumar, Manish Tomar, and Anish Tadimarri. "AI-driven marketing: Transforming sales processes for success in the digital age." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.2 (2023): 250-260.
  8. Abouelyazid, Mahmoud. "Natural Language Processing for Automated Customer Support in E-Commerce: Advanced Techniques for Intent Recognition and Response Generation." Journal of AI-Assisted Scientific Discovery 2.1 (2022): 195-232.
  9. Prabhod, Kummaragunta Joel. "Utilizing Foundation Models and Reinforcement Learning for Intelligent Robotics: Enhancing Autonomous Task Performance in Dynamic Environments." Journal of Artificial Intelligence Research 2.2 (2022): 1-20.
  10. Tatineni, Sumanth, and Anirudh Mustyala. "AI-Powered Automation in DevOps for Intelligent Release Management: Techniques for Reducing Deployment Failures and Improving Software Quality." Advances in Deep Learning Techniques 1.1 (2021): 74-110.