(De)-Indexing and the Right to be Forgotten

2025-01-07

AI Summary

The implementation of the “Right to Be Forgotten” (RTBF) in the digital age faces tension between privacy protection and information accessibility, necessitating reconciliation between legal rights and retrieval system practices. Method: This paper systematically analyzes the intrinsic relationship between the evolution of information retrieval (IR) models—Boolean, probabilistic, vector space, and embedding-based—and search engine de-indexing mechanisms, delineating technical response boundaries for deletion requests across models; it further investigates the auxiliary role of large language models (LLMs) in semantic content understanding and de-indexing decision-making. Contribution/Results: We propose an interdisciplinary explanatory framework tailored for policymakers and the public, translating abstract legal rights into actionable technical consensus. The framework clarifies engineering constraints, identifies collaborative implementation pathways, and advances RTBF from principle to pragmatic governance.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Web search models and ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Social and technical mechanisms of refusal for web technologies and applications

Abstract

In the digital age, the challenge of forgetfulness has emerged as a significant concern, particularly regarding the management of personal data and its accessibility online. The right to be forgotten (RTBF) allows individuals to request the removal of outdated or harmful information from public access, yet implementing this right poses substantial technical difficulties for search engines. This paper aims to introduce non-experts to the foundational concepts of information retrieval (IR) and de-indexing, which are critical for understanding how search engines can effectively"forget"certain content. We will explore various IR models, including boolean, probabilistic, vector space, and embedding-based approaches, as well as the role of Large Language Models (LLMs) in enhancing data processing capabilities. By providing this overview, we seek to highlight the complexities involved in balancing individual privacy rights with the operational challenges faced by search engines in managing information visibility.

Problem

• Digital Age
• Right to be Forgotten
• Online Data Management

Innovation

• Information Retrieval Models
• Large Language Models
• Right to be Forgotten Implementation