New research proposes 'societal relevance' to filter misinformation in search & AI
A concept that could reshape how search engines and AI handle harmful content
This paper examines "societal relevance," a concept introduced by Haider and Sundin to address limitations of topical and user relevance in web search. The study investigates three analytical questions: the definition of societal relevance, its practical application in search systems, and its distinction from information quality measures. By analyzing combinations of system, user, and societal relevance, it explores how search outputs can be optimized for the "greater good." The concept provides a vital framework for developing value-driven search engines that prioritize ethical outcomes and societal interests over mere keyword matching.
- Defines 'societal relevance' as a new dimension beyond topicality and user relevance for filtering harmful content
- Proposes combining system, user, and societal relevance to optimize search outputs for the 'greater good'
- Distinguishes societal relevance from information quality measures, offering a distinct framework for ethical search design
Why It Matters
Professionals could see search and AI tools that actively suppress misinformation, not just match keywords.