Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Control Search Rankings, Control the World: What is a Good Search Engine?

Simon Coghlan, Hui Xian Chia, Falk Scholer, Damiano Spina

TL;DR

This paper investigates what constitutes a 'good' web search engine from an ethics-informed IR perspective. It introduces four role-based models (Customer Servant, Librarian, Journalist, Teacher) to analyze SE behavior and links them to IR methods, including learning-to-rank and fairness-aware ranking, illustrated through a COVID-19 case study. The analysis clarifies autonomy versus societal protection, discusses the rise of LLM-based conversational search, and addresses accountability and regulatory considerations. By providing a structured, interdisciplinary lens, it aims to guide regulators, designers, and researchers in shaping ethical search design as AI-enabled systems become more capable. The work emphasizes that there is no single universally good SE, but a spectrum of ethically justifiable approaches contingent on context and governance frameworks.

Abstract

This paper examines the ethical question, 'What is a good search engine?' Since search engines are gatekeepers of global online information, it is vital they do their job ethically well. While the Internet is now several decades old, the topic remains under-explored from interdisciplinary perspectives. This paper presents a novel role-based approach involving four ethical models of types of search engine behavior: Customer Servant, Librarian, Journalist, and Teacher. It explores these ethical models with reference to the research field of information retrieval, and by means of a case study involving the COVID-19 global pandemic. It also reflects on the four ethical models in terms of the history of search engine development, from earlier crude efforts in the 1990s, to the very recent prospect of Large Language Model-based conversational information seeking systems taking on the roles of established web search engines like Google. Finally, the paper outlines considerations that inform present and future regulation and accountability for search engines as they continue to evolve. The paper should interest information retrieval researchers and others interested in the ethics of search engines.

Control Search Rankings, Control the World: What is a Good Search Engine?

TL;DR

This paper investigates what constitutes a 'good' web search engine from an ethics-informed IR perspective. It introduces four role-based models (Customer Servant, Librarian, Journalist, Teacher) to analyze SE behavior and links them to IR methods, including learning-to-rank and fairness-aware ranking, illustrated through a COVID-19 case study. The analysis clarifies autonomy versus societal protection, discusses the rise of LLM-based conversational search, and addresses accountability and regulatory considerations. By providing a structured, interdisciplinary lens, it aims to guide regulators, designers, and researchers in shaping ethical search design as AI-enabled systems become more capable. The work emphasizes that there is no single universally good SE, but a spectrum of ethically justifiable approaches contingent on context and governance frameworks.

Abstract

This paper examines the ethical question, 'What is a good search engine?' Since search engines are gatekeepers of global online information, it is vital they do their job ethically well. While the Internet is now several decades old, the topic remains under-explored from interdisciplinary perspectives. This paper presents a novel role-based approach involving four ethical models of types of search engine behavior: Customer Servant, Librarian, Journalist, and Teacher. It explores these ethical models with reference to the research field of information retrieval, and by means of a case study involving the COVID-19 global pandemic. It also reflects on the four ethical models in terms of the history of search engine development, from earlier crude efforts in the 1990s, to the very recent prospect of Large Language Model-based conversational information seeking systems taking on the roles of established web search engines like Google. Finally, the paper outlines considerations that inform present and future regulation and accountability for search engines as they continue to evolve. The paper should interest information retrieval researchers and others interested in the ethics of search engines.

Paper Structure

This paper contains 18 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Search Engines
  3. How Search Engines Work
  4. Evaluation of Search Engines in Information Retrieval
  5. Search Engines as Providers of Information
  6. Four Ethical Search Engine Models
  7. Model 1: Customer Servant.
  8. Model 2: Librarian.
  9. Model 3: Journalist.
  10. Model 4: Teacher.
  11. Remarks on the Four Models
  12. Implementing Ethical Models in Search Engines
  13. COVID-19 Pandemic Case Study
  14. Discussion
  15. Accountability of Private Platforms
  16. ...and 3 more sections

Figures (1)

  • Figure 1: A schematic representation of the relation between the four ethical models, the degree of intervention -- both inferential (related to guessing relevance) and ethical (related to what is considered good for the user and/or society) -- that models take into account, and examples of instances of IR techniques throughout that spectrum.