  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.
