Abstract
AbstractThis research examines the considerations of responsible Artificial Intelligence in the deployment of AI-based COVID-19 digital proximity tracking and tracing applications in two countries; the State of Qatar and the United Kingdom. Based on the alignment level analysis with the Good AI Society’s framework and sentiment analysis of official tweets, the diagnostic analysis resulted in contrastive findings for the two applications. While the application EHTERAZ (Arabic for precaution) in Qatar has fallen short in adhering to the responsible AI requirements, it has contributed significantly to controlling the pandemic. On the other hand, the UK’s NHS COVID-19 application has exhibited limited success in fighting the virus despite relatively abiding by these requirements. This underlines the need for obtaining a practical and contextual view for a comprehensive discourse on responsible AI in healthcare. Thereby offering necessary guidance for striking a balance between responsible AI requirements and managing pressures towards fighting the pandemic.
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Information Systems,Theoretical Computer Science,Software
Reference79 articles.
1. Adhikary, T., Jana, A. D., Chakrabarty, A., & Jana, S. K. (2019). The internet of things (iot) augmentation in healthcare: An application analytics. In International conference on intelligent computing and communication technologies (pp. 576–583). Springer.
2. Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J. (2013). Artificial neural networks in medical diagnosis. Journal of Applied Biomedicine, 11(2), 47–58.
3. Apple (2020). Privacy-preserving contact tracing 2020. Available from: https://covid19.apple.com/contacttracing. Accessed 20 Nov 2020.
4. Beauchamp, T. L., & Childress, J. F. (2001). Principles of biomedical ethics. Oxford University Press.
5. Bennett, C. C., & Hauser, K. (2013). Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach. Artificial Intelligence in Medicine, 57(1), 9–19.
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