Infomation Design Research on AI Algorithmic Bias : Design Strategies for Preventing Echo Chambers

다국어 초록 (Multilingual Abstract)

In the digital environment, AI algorithms serve as the core technology for optimizing user experience; however, biased data learning and design flaws often exacerbate the echo chamber phenomenon by overemphasizing specific information. This limits information diversity and contributes to social conflict and polarization. Accordingly, this study aims to analyze the bias inherent in AI algorithms and explore information design strategies to mitigate such bias, thereby establishing an information delivery framework that prevents echo chambers. The research methodology for studying information design to counteract echo chambers caused by AI algorithmic bias consists of three stages. First, a structural analysis is conducted to examine how AI algorithm bias forms and reinforces echo chambers. This includes identifying problems that occur in the filtering and recommendation processes and exploring design-based approaches to minimize bias. Second, from the perspective of information design, strategies for preventing echo chambers are investigated. Visual, structural, and interactive design elements are utilized to maximize information diversity and enable users to access balanced perspectives through intuitive interface designs. Finally, a practical design framework is developed that can be applied to public policy platforms, media systems, and memory-based AI interfaces, ensuring real-world usability. Through this approach, the study seeks to establish a design model that enables AI technologies to regain social trust and contribute to a fair and inclusive information ecosystem. Ultimately, this research emphasizes that information design can play a pivotal role in aligning AI systems with human values, fostering transparency, diversity, and equity in the digital information environment.