# Bias in Conversational Search: The Double-Edged Sword of the Personalized Knowledge Graph

arXiv: https://arxiv.org/abs/2010.10409 · HTML: https://mlsanity.com/paper/2010.10409/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankers](https://mlsanity.com/paper/2104.13640/) (2021)
- [A Survey on Recent Advances in Conversational Data Generation](https://mlsanity.com/paper/2405.13003/) (2024)
- [An Ecosystem for Personal Knowledge Graphs: A Survey and Research Roadmap](https://mlsanity.com/paper/2304.09572/) (2023)
- [Mitigating the Position Bias of Transformer Models in Passage Re-Ranking](https://mlsanity.com/paper/2101.06980/) (2021)
- [Learning to Ask: Conversational Product Search via Representation Learning](https://mlsanity.com/paper/2411.14466/) (2024)
- [Analyzing Race and Country of Citizenship Bias in Wikidata](https://mlsanity.com/paper/2108.05412/) (2021)
- [Mitigating Relational Bias on Knowledge Graphs](https://mlsanity.com/paper/2211.14489/) (2022)
- [The Lifecycle of "Facts": A Survey of Social Bias in Knowledge Graphs](https://mlsanity.com/paper/2210.03353/) (2022)
- [Engineering Conversational Search Systems: A Review of Applications, Architectures, and Functional Components](https://mlsanity.com/paper/2407.00997/) (2024)
- [A Survey on Conversational Search and Applications in Biomedicine](https://mlsanity.com/paper/2211.15328/) (2022)

## Abstract (arXiv)

Conversational AI systems are being used in personal devices, providing users with highly personalized content. Personalized knowledge graphs (PKGs) are one of the recently proposed methods to store users' information in a structured form and tailor answers to their liking. Personalization, however, is prone to amplifying bias and contributing to the echo-chamber phenomenon. In this paper, we discuss different types of biases in conversational search systems, with the emphasis on the biases that are related to PKGs. We review existing definitions of bias in the literature: people bias, algorithm bias, and a combination of the two, and further propose different strategies for tackling these biases for conversational search systems. Finally, we discuss methods for measuring bias and evaluating user satisfaction.