Privacy, Bias, and Transparency: Analysing User Perceptions and Trustworthiness in AI Integrated Domestic Technologies Adoption

International Journal of Computer Science and Engineering

© 2024 by SSRG - IJCSE Journal

Volume 11 Issue 8

Year of Publication : 2024

Authors : Sanat Punj

10.14445/23488387/IJCSE-V11I8P104

How to Cite?

Sanat Punj, "Privacy, Bias, and Transparency: Analysing User Perceptions and Trustworthiness in AI Integrated Domestic Technologies Adoption," SSRG International Journal of Computer Science and Engineering , vol. 11,  no. 8, pp. 26-36, 2024. Crossref, https://doi.org/10.14445/23488387/IJCSE-V11I8P104

Abstract:

Artificial Intelligence (AI) models have become more sophisticated than ever, as their performance optimizes at a groundbreaking rate with the rapid advancement of technology. Increasing AI adoption amplifies the need for trustworthy systems and may threaten social confidence, reflecting negative user perceptions. This could heighten threats posed by privacy concerns, transparency of AI systems, and data biases. The present study aims to statistically evaluate user trust and attitudes towards AI-integrated domestic (ubiquitous) technologies by analyzing privacy concerns, transparency of AI systems, and the data bias they are prone to. Broadly, this research is intended to understand the vulnerabilities developed by the use of common, domestic AI systems on users based on several parameters. A quantitative primary study was conducted by surveying 40 financially stable individuals of diverse age groups (10-80), nationalities (Indonesia, India, and USA), educational qualifications, and genders. This research analysis elucidates the critical factors within specific AI-domestic models that drive user trustworthiness, offering valuable insights for businesses. The research could be utilized to guide R&D direction for businesses, enhance the robustness of AI systems, improve AI user experience for society, and increase user retention rates for such technologies.

Keywords:

Artificial Intelligence, Machine Learning, Bias, Transparency, Data privacy

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