On the Social and Technical Challenges of Web Search Autosuggestion Moderation

TL;DR

This paper discusses the ongoing social and technical challenges in moderating web search autosuggestions, highlighting persistent issues with biased or inappropriate suggestions despite existing mitigation efforts.

Contribution

It provides a comprehensive analysis of the challenges and complexities in detecting and addressing problematic autosuggestions in web search engines.

Findings

Persistent biases in autosuggestions remain despite mitigation efforts.

Multiple dimensions and stages of the suggestion pipeline pose challenges.

The issues extend to various applications beyond web search.

Abstract

Past research shows that users benefit from systems that support them in their writing and exploration tasks. The autosuggestion feature of Web search engines is an example of such a system: It helps users in formulating their queries by offering a list of suggestions as they type. Autosuggestions are typically generated by machine learning (ML) systems trained on a corpus of search logs and document representations. Such automated methods can become prone to issues that result in problematic suggestions that are biased, racist, sexist or in other ways inappropriate. While current search engines have become increasingly proficient at suppressing such problematic suggestions, there are still persistent issues that remain. In this paper, we reflect on past efforts and on why certain issues still linger by covering explored solutions along a prototypical pipeline for identifying, detecting, and addressing problematic autosuggestions. To showcase their complexity, we discuss several dimensions of problematic suggestions, difficult issues along the pipeline, and why our discussion applies to the increasing number of applications beyond web search that implement similar textual suggestion features. By outlining persistent social and technical challenges in moderating web search suggestions, we provide a renewed call for action.