Cornell University

This research examines the ethical challenges posed by big data systems, with a particular focus on the intersection of data collection, privacy, and environmental impact. The study analyzes how massive data collection and processing affect both individual privacy and environmental sustainability, proposing a framework for responsible data practices that considers both ethical and ecological implications. The authors present guidelines for ethical data governance that balance innovation needs with social responsibility and environmental sustainability.

This research examines how artificial intelligence is transforming assistive technologies, creating new opportunities and challenges for users with disabilities. The study analyzes various AI-powered assistive technologies, including advanced screen readers, intelligent voice interfaces, and computer vision systems for the visually impaired. The authors identify key success factors and potential pitfalls in developing AI-based assistive technologies, providing guidelines for creating more effective and inclusive solutions.

This comprehensive study examines methods for developing machine learning systems that protect individual privacy while maintaining high performance. The research analyzes various privacy-preserving techniques including differential privacy, federated learning, and secure multi-party computation. The authors provide practical guidelines for implementing privacy-preserving ML systems and evaluate the trade-offs between privacy guarantees and model utility. The paper also addresses emerging challenges in privacy-preserving ML, including new attack vectors and regulatory compliance requirements.