Trustworthy AI in the Age of Pervasive Computing and Big Data
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Abstract (arXiv)
The era of pervasive computing has resulted in countless devices that continuously monitor users and their environment, generating an abundance of user behavioural data. Such data may support improving the quality of service, but may also lead to adverse usages such as surveillance and advertisement. In parallel, Artificial Intelligence (AI) systems are being applied to sensitive fields such as healthcare, justice, or human resources, raising multiple concerns on the trustworthiness of such systems. Trust in AI systems is thus intrinsically linked to ethics, including the ethics of algorithms, the ethics of data, or the ethics of practice. In this paper, we formalise the requirements of trustworthy AI systems through an ethics perspective. We specifically focus on the aspects that can be integrated into the design and development of AI systems. After discussing the state of research and the remaining challenges, we show how a concrete use-case in smart cities can benefit from these methods.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jan 2020.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 3; 2025: 0; 2026: 1.
9 papers.
- 2021Trustworthy AI: From Principles to Practicescited by 206
- 2021Software Engineering for AI-Based Systems: A Surveycited by 68
- 2024Unleashing the Power of Continual Learning on Non-Centralized Devices: A Surveycited by 23
- 2021Personalized Federated Deep Learning for Pain Estimation From Face Imagescited by 8
- 2024Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approachcited by 3
- 2022A Seven-Layer Model for Standardising AI Fairness Assessmentcited by 1
- 2023Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic reviewcited by 0
- 2024Trustworthy XAI and Applicationcited by 0
- 2026Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainabilitycited by 0
- 2026Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability3 Aug 2026
- 2024Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey18 Dec 2024
- 2024Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach28 Oct 2024
- 2024Trustworthy XAI and Application22 Oct 2024
- 2023Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review18 Aug 2023
- 2022A Seven-Layer Model for Standardising AI Fairness Assessment21 Dec 2022
- 2021Trustworthy AI: From Principles to Practices4 Oct 2021
- 2021Software Engineering for AI-Based Systems: A Survey5 May 2021
- 2021Personalized Federated Deep Learning for Pain Estimation From Face Images12 Jan 2021