Framework for Ethical Acquisition of User-Data to Improve Recommendation Models’ Accuracy in Digital Systems

The modern digital ecosystem has evolved into a pervasive, opaque system where platforms collect and infer personal data through nearly every online action, search queries, email content, browsing history, and app usage, without transparency. Justified as a means to deliver “relevant” content and ads, this approach undermines user privacy, introduces bias, and normalizes surveillance. Through a comprehensive literature review, this study sought to: critically analyze the current landscape of user tracking, profiling, and privacy violations in online platforms and to evaluate the impact of existing legal, technical, and platform-driven mechanisms like GDPR, CCPA, ATT, and Privacy Sandbox in protecting user autonomy. It was learned that the current frameworks fall short by mostly being policy-based and having hard-to-access user controls. And a major flaw in existing systems is the assumption that all digital behavior reflects actual user preference, overlooking shared devices, accidental clicks, and non-user actions. To validate these insights, a survey of 572 privacy-aware participants was conducted, where nearly 71% preferring a proactive solution over passive regulatory frameworks and hard-to-navigate privacy menus/dashboards. Building on these findings, this study proposes a framework: a digital platform where individuals actively create and manage modular preference profiles, categorized by app type or content domain, which can be selectively and consensually shared with platforms in a standardized format. This concept facilitates high-quality, context-rich datasets for algorithms, enhancing personalization and recommendation models’ accuracy and performance. By shifting from forced surveillance to invited participation, this approach advances ethical data-sourcing, enhances algorithmic accuracy, and aligns with SDG 9 and SDG 16 by prioritizing responsible digital solutions, process innovation, and safeguarding user autonomy.

@article{Qazi2025,
  title     = {Framework for Ethical Acquisition of User-Data to Improve Recommendation Models’ Accuracy in Digital Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Shaheer Hussain Qazi and M.Batumalay and Asheer Hussain and Ali Abbas},
  doi       = {10.14569/IJACSA.2025.0161238},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161238}
}