Earticle

초록

영어

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.

목차

Abstract
1. Introduction
2. Theoretical Background
2.1 Concept and Causes of AI Algorithmic Bias
2.2 Differences Between Echo Chamber and Filter Bubble
2.3 The Role of Infomation Design
3. Survey on Awareness of Bias
3.1 Research Method
3.2 Research Method
3.3 Results of Awareness Survey
4. Categories of Design Research on Algorithmic Bias
4.1 Visualization of Balanced Information
4.2 Transparency-Oriented Interface
4.3 Diversity Nudge
5. Conclusion
Acknowledgement
References

키워드

저자

• Keunyoung Yang [ Research Prof. Institute of Design, Inje Univ., Gimhae, Korea ]
• Yena Bae [ Prof. of U-Design, Inje Univ., Gimhae, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380