skip to main content
10.1145/3477495.3531682acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
abstract

Fairness-Aware Question Answering for Intelligent Assistants

Published: 07 July 2022 Publication History

Abstract

Conversational intelligent assistants, such as Amazon Alexa, Google Assistant, and Apple Siri, are a form of voice-only Question Answering (QA) system and have the potential to address complex information needs. However, at the moment they are mostly limited to answering with facts expressed in a few words. For example, when a user asks Google Assistant if coffee is good for their health, it responds by justifying why it is good for their health without shedding any light on the side effects coffee consumption might have \citegao2020toward. Such limited exposure to multiple perspectives can lead to change in perceptions, preferences, and attitude of users, as well as to the creation and reinforcement of undesired cognitive biases. Getting such QA systems to provide a fair exposure to complex answers -- including those with opposing perspectives -- is an open research problem. In this research, I aim to address the problem of fairly exposing multiple perspectives and relevant answers to users in a multi-turn conversation without negatively impacting user satisfaction.

References

[1]
Ruoyuan Gao and Chirag Shah. 2020. Toward Creating a Fairer Ranking in Search Engine Results. Information Processing & Management, Vol. 57, 1 (2020), 102138. https://doi.org/10.1016/j.ipm.2019.102138
[2]
Sachin Pathiyan Cherumanal, Damiano Spina, Falk Scholer, and W. Bruce Croft. 2021. Evaluating Fairness in Argument Retrieval. In Proc. CIKM. 3363--3367. https://doi.org/10.1145/3459637.3482099
[3]
Sachin Payhiyan Cherumnala, Damiano Spina, Falk Scholer, and W. Bruce Croft. 2022. RMIT at TREC 2021 Fair Ranking Track. In Proc. TREC . https://trec.nist.gov/pubs/trec30/papers/RMIT-IR-F.pdf
[4]
Piotr Sapiezynski, Wesley Zeng, Ronald E Robertson, Alan Mislove, and Christo Wilson. 2019. Quantifying the Impact of User Attention on Fair Group Representation in Ranked Lists. In Proc. WWW. 553--562. https://doi.org/10.1145/3308560.3317595
[5]
Ke Yang and Julia Stoyanovich. 2017. Measuring Fairness in Ranked Outputs. In Proc. SSDBM. Article 22, https://doi.org/10.1145/3085504.3085526

Cited By

View all
  • (2024)Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational SearchAdjunct Proceedings of the 26th International Conference on Mobile Human-Computer Interaction10.1145/3640471.3680245(1-10)Online publication date: 21-Sep-2024
  • (2023)Envisioning Equitable Speech Technologies for Black Older AdultsProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594005(379-388)Online publication date: 12-Jun-2023

Index Terms

  1. Fairness-Aware Question Answering for Intelligent Assistants

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2022
    3569 pages
    ISBN:9781450387323
    DOI:10.1145/3477495
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 July 2022

    Check for updates

    Author Tags

    1. conversational search
    2. fairness
    3. information retrieval

    Qualifiers

    • Abstract

    Funding Sources

    Conference

    SIGIR '22
    Sponsor:

    Acceptance Rates

    Overall Acceptance Rate 792 of 3,983 submissions, 20%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)31
    • Downloads (Last 6 weeks)7
    Reflects downloads up to 28 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational SearchAdjunct Proceedings of the 26th International Conference on Mobile Human-Computer Interaction10.1145/3640471.3680245(1-10)Online publication date: 21-Sep-2024
    • (2023)Envisioning Equitable Speech Technologies for Black Older AdultsProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594005(379-388)Online publication date: 12-Jun-2023

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media