When Are Search Completion Suggestions Problematic?
By Alexandra Olteanu, Fernando Diaz, Gabriella Kazai. 2020
37 citations recorded by the source corpus.
Problematic web search query completion suggestions-perceived as biased, offensive, or in some other way harmful-can reinforce existing stereotypes and misbeliefs, and even nudge users towards undesirable patterns of behavior. Locating such suggestions is difficult, not only due to the long-tailed nature of web search, but also due to differences in how people assess potential harms. Grounding our study in web search query logs, we explore when system-provided suggestions might be perceived as problematic through a series of crowd-experiments where we systematically manipulate: the search query fragments provided by users, possible user search intents, and the list of query completion suggestions. To examine why query suggestions might be perceived as problematic, we contrast them to an inventory of known types of problematic suggestions. We report our observations around differences in the prevalence of a) suggestions that are problematic on their own versus b) suggestions that are problematic for the query fragment provided by a user, for both common informational needs and in the presence of web search voids-topics searched by few to no users. Our experiments surface a rich array of scenarios where suggestions are considered problematic, including due to the context in which they were surfaced. Compounded by the elusive nature of many such scenarios, the prevalence of suggestions perceived as problematic only for certain user inputs, raises concerns about blind spots due to data annotation practices that may lead to some types of problematic suggestions being overlooked.
Subfields: Computer Science Applications, Information Systems, Sociology and Political Science, Natural Language Processing
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In Computer Science Applications
In Information Systems
In Sociology and Political Science
In Natural Language Processing
- Su Lin Blodgett
- Hanna M. Wallach
- Alexandra Olteanu
- Yulia Tsvetkov
- Andrew McCallum
- Charles Sutton
- Yejin Choi
- Mark W. Schmidt
Citations in the corpus
- Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets (2021, background): “Auditing NLP systems for computational harms like the reproduction of stereotypes or hate speech remains a persistent challenge, due in no small part to the deeply contextual and open nature of language use and tasks (Austin, 1975; Clark, 1996; Howcroft et al., 2020; Abid et al., 2021; Olteanu et al”