When Are Search Completion Suggestions Problematic?
Organized by ACM
CSCW 2020 Honorable Mention
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.
Auditing natural language processing (NLP) systems for computational harms remains an elusive goal. Doing so, however, is critical as there is a proliferation of language technologies (and applications) that are enabled by increasingly powerful natural language generation and representation models. Computational harms occur not only due to what content is being produced by people, but also due to how content is being embedded, represented, and generated by large-scale and sophisticated language models. This webinar will cover challenges with locating and measuring potential harms that language technologies—and the data they ingest or generate—might surface, exacerbate, or cause. Such harms can range from more overt issues, like surfacing offensive speech or reinforcing stereotypes, to more subtle issues, like nudging users toward undesirable patterns of behavior or triggering memories of traumatic events. Join Microsoft researchers Su Lin Blodgett and Alexandra Olteanu, from the FATE Group at Microsoft Research Montréal, to examine pitfalls in some state-of-the-art approaches to measuring computational harms in language technologies. For such measurements of harms to be effective, it is important to clearly articulate both: 1) the construct to be measured and 2) how the measurements operationalize that construct. The webinar will also overview possible approaches practitioners could take to proactively identify issues that might not be on their radar, and thus effectively track and measure a wider range of issues. Together, you'll explore: Possible pitfalls when measuring computational harms in language technologies Challenges to identifying what harms we should be measuring Steps toward anticipating computational harms Resource list: A Critical Survey of “Bias” in NLP (Publication) When Are Search Completion Suggestions Problematic? (Publication) Social Data (Publication) Characterizing Problematic Email Reply Suggestions (Publication) Overcoming Failures of Imagination in AI Infused System Development and Deployment (Publication) Defining Bias with Su Lin Blodgett (Podcast) Language, Power and NLP (Podcast) Su Lin Blodgett (researcher profile) Alexandra Olteanu (researcher profile) *This on-demand webinar features a previously recorded Q&A session and open captioning. Explore more Microsoft Research webinars: https://aka.ms/msrwebinars