Ethical and Privacy Implications of Machine Learning in Android Development

Abstract

This article examines the ethical and privacy implications of machine learning integration in Android application development. It explores how ML-powered applications transform data collection and processing, creating unprecedented privacy vulnerabilities while enabling advanced functionalities. Through detailed case studies across healthcare, social media, and e-commerce applications, the article illustrates varied implementation practices and their impacts on user privacy. It identifies sources of algorithmic bias within Android ML systems and evaluates their disproportionate effects on marginalized communities, while assessing technical solutions, including federated learning and differential privacy frameworks. The article evaluates multi-stakeholder governance models, industry standards, and educational initiatives necessary for responsible ML deployment. By connecting technical implementation details with broader social implications, the article provides a comprehensive ethical framework for Android developers, policymakers, and users navigating the complex landscape of machine learning in mobile ecosystems.