Year: 2025
Type: article
Study design: Observational Study
Abstract: Cloud-based machine learning systems are increasingly used in sectors such as healthcare, finance, and public services, where they influence decisions with significant social consequences. While these technologies offer scalability and efficiency, they raise significant concerns regarding security, privacy, and compliance. One of the central issues is algorithmic bias, which can emerge from data, design choices, or system interactions, and is often amplified when deployed at scale through cloud infrastructures. This study examines the relationship between algorithmic bias, social equity, and cloud-based innovation. Drawing on a survey of public perceptions, we find strong recognition of the risks posed by biased systems, including diminished trust, harm to vulnerable populations, and erosion of fairness. Participants overwhelmingly supported regulatory oversight, developer accountability, and greater transparency in algorithmic decision-making. Building on these findings, this paper proposes measures to integrate fairness auditing, representative datasets, and bias mitigation techniques into cloud security and compliance frameworks. We argue that addressing bias is not only an ethical responsibility but also an essential requirement for safeguarding public trust and meeting evolving legal and regulatory standards.
Source: Standards
Authors: Amal Alhosban, Ritik Gaire, Hassan Al-Ababneh
Institutions: University of Michigan–Flint, Zarqa University
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,207
OpenAlex ID: W7117301770