On the social and technical challenges of Web search autosuggestion moderation

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 formulate their queries by offering a list of suggestions as they type. Such autosuggestions are typically generated by machine learning (ML) systems trained on a corpus of search logs and document representations. These automated methods can however become prone to issues that might result in the system making problematic suggestions that are biased, racist, sexist or in other ways inappropriate. While current search engines have become increasingly proficient at suppressing many types of 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 mitigating problematic autosuggestions. To showcase their complexity, we discuss several dimensions of problematic suggestions, difficult issues along the pipeline, and why our discussion applies to an increasing number of applications (beyond Web search) that implement similar textual suggestion features. By outlining several persistent social and technical challenges in moderating Web search suggestions, we hope to provide a renewed call for action.