Sachin Pathiyan Cherumanal
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.
- 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.2019.102138Google Scholar
Digital Library
- 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/10.1145/3459637.3482099Google ScholarDigital Library
- 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.pdfGoogle Scholar
- 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/10.1145/3308560.3317595Google ScholarDigital Library
- Ke Yang and Julia Stoyanovich. 2017. Measuring Fairness in Ranked Outputs. In Proc. SSDBM. Article 22, https://doi.org/10.1145/3085504.3085526Google ScholarDigital Library
Index Terms
- Fairness-Aware Question Answering for Intelligent Assistants
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