Exploring human-data interaction: an AI-enhanced systematic mapping

Living in a modern society driven by data underscores the significance of Human-data interaction (HDI). HDI is at the intersection of computer science, statistics, sociology, psychology, and behavioral studies, and is crucial in a landscape where seemingly ’free’ products often capitalize on users as commodities. It is important to ensure robust protection mechanisms for personal data, as the quality and ease of human interaction with the surrounding data shape our knowledge. The research explores HDI to understand how people perceive and interact with data, with the aim of improving decision-making and refining interaction within this context. The text has been improved to adhere to the following characteristics: objectivity, comprehensibility and logical structure, conventional structure, clear and objective language, format, formal register, structure, balance, precise word choice, and grammatical correctness. The primary goal is to generate insights that facilitate informed decision-making by understanding human engagement with data. In addition, our goal is to improve interaction within the context of HDI. To establish a knowledge foundation, we conducted a systematic review of HDI research over the past decade, consolidating essential knowledge. The article outlines the introduction of HDI, details the methodology of the systematic review, and presents the results obtained. The systematic study provides comprehensive insights into HDI, addressing key research questions. The findings illuminate human interaction with data, contributing to nuanced understandings of information dissemination and user engagement. The results offer a valuable resource for future studies, providing a well-rounded perspective on HDI. This article contributes an introductory exploration of HDI, outlines systematic study methodology, and presents outcomes to answer research questions. The importance of understanding human data interaction for informed decision-making is underscored by the findings. The synthesized knowledge can serve as a foundational stepping stone for future research in the dynamic realm of HDI.