Ethical

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 foundational paper examines the ethical implications of artificial intelligence development and deployment. The authors present a comprehensive framework for ensuring AI systems are developed and used in ways that benefit humanity. The research addresses key ethical challenges including algorithmic bias, transparency, accountability, and the long-term societal impact of AI systems. The paper proposes concrete guidelines for ethical AI development and governance structures to ensure responsible innovation.

A randomized field experiment conducted on X (formerly Twitter) in 2023 examined the political effects of the platform’s feed algorithm. Researchers assigned active US-based users randomly to either an algorithmic or chronological feed for 7 weeks, measuring political attitudes and online behaviour. The study found that switching from a chronological to an algorithmic feed increased engagement and shifted political opinion towards more conservative positions, particularly regarding policy priorities, perceptions of criminal investigations into Donald Trump, and views on the war in Ukraine. The algorithm promotes conservative content and demotes posts by traditional media. Participants exposed to the algorithmic feed were more likely to follow conservative political activist accounts, which they continued to follow even after switching off the algorithm. In contrast, switching from the algorithmic to the chronological feed had no comparable effects on political attitudes, suggesting that initial exposure to the algorithm has persistent effects on users’ political attitudes and account-following behaviour.