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Evaluating Privacy, Security, and Trust Perceptions in Conversational AI: A Systematic Review

Anna Leschanowsky, Silas Rech, Birgit Popp, Tom Bäckström

TL;DR

The paper addresses how users perceive privacy, security, and trust in conversational AI (CAI) systems and how these perceptions influence adoption and usage. It employs two PRISMA-based systematic literature reviews to map CAI-related findings, constructs, scales, and measurement practices, revealing extensive use of TAM/UTAUT frameworks and substantial overlap among privacy, security, and trust concepts. Key contributions include an synthesis of 100 privacy/security studies and 58 trust studies, a taxonomy of constructs and sub-constructs, an assessment of scale reliability and validity, and guidance toward a unified CAI-specific measurement framework. The study highlights the need for clearer distinctions between privacy and security, broader cross-domain research, and consideration of CAI advances such as LLMs to build trustworthy, privacy-preserving CAI systems with robust measurement foundations.

Abstract

Conversational AI (CAI) systems which encompass voice- and text-based assistants are on the rise and have been largely integrated into people's everyday lives. Despite their widespread adoption, users voice concerns regarding privacy, security and trust in these systems. However, the composition of these perceptions, their impact on technology adoption and usage and the relationship between privacy, security and trust perceptions in the CAI context remain open research challenges. This study contributes to the field by conducting a Systematic Literature Review and offers insights into the current state of research on privacy, security and trust perceptions in the context of CAI systems. The review covers application fields and user groups and sheds light on empirical methods and tools used for assessment. Moreover, it provides insights into the reliability and validity of privacy, security and trust scales, as well as extensively investigating the subconstructs of each item as well as additional concepts which are concurrently collected. We point out that the perceptions of trust, privacy and security overlap based on the subconstructs we identified. While the majority of studies investigate one of these concepts, only a few studies were found exploring privacy, security and trust perceptions jointly. Our research aims to inform on directions to develop and use reliable scales for users' privacy, security and trust perceptions and contribute to the development of trustworthy CAI systems.

Evaluating Privacy, Security, and Trust Perceptions in Conversational AI: A Systematic Review

TL;DR

The paper addresses how users perceive privacy, security, and trust in conversational AI (CAI) systems and how these perceptions influence adoption and usage. It employs two PRISMA-based systematic literature reviews to map CAI-related findings, constructs, scales, and measurement practices, revealing extensive use of TAM/UTAUT frameworks and substantial overlap among privacy, security, and trust concepts. Key contributions include an synthesis of 100 privacy/security studies and 58 trust studies, a taxonomy of constructs and sub-constructs, an assessment of scale reliability and validity, and guidance toward a unified CAI-specific measurement framework. The study highlights the need for clearer distinctions between privacy and security, broader cross-domain research, and consideration of CAI advances such as LLMs to build trustworthy, privacy-preserving CAI systems with robust measurement foundations.

Abstract

Conversational AI (CAI) systems which encompass voice- and text-based assistants are on the rise and have been largely integrated into people's everyday lives. Despite their widespread adoption, users voice concerns regarding privacy, security and trust in these systems. However, the composition of these perceptions, their impact on technology adoption and usage and the relationship between privacy, security and trust perceptions in the CAI context remain open research challenges. This study contributes to the field by conducting a Systematic Literature Review and offers insights into the current state of research on privacy, security and trust perceptions in the context of CAI systems. The review covers application fields and user groups and sheds light on empirical methods and tools used for assessment. Moreover, it provides insights into the reliability and validity of privacy, security and trust scales, as well as extensively investigating the subconstructs of each item as well as additional concepts which are concurrently collected. We point out that the perceptions of trust, privacy and security overlap based on the subconstructs we identified. While the majority of studies investigate one of these concepts, only a few studies were found exploring privacy, security and trust perceptions jointly. Our research aims to inform on directions to develop and use reliable scales for users' privacy, security and trust perceptions and contribute to the development of trustworthy CAI systems.
Paper Structure (53 sections, 4 figures, 16 tables)

This paper contains 53 sections, 4 figures, 16 tables.

Table of Contents

  1. Introduction
  2. Related Work and Contributions
  3. Method
  4. Search Strategy
  5. Study Selection
  6. Analysis Procedure
  7. Application Fields and Devices, Research Methods
  8. Selected Papers
  9. Time Trend
  10. Geographical Distribution
  11. Devices and Modalities
  12. Application Fields
  13. Research Methods
  14. Recruitment Methods
  15. Participants
  16. ...and 38 more sections

Figures (4)

  • Figure 1: Wordcloud as an illustration of the used terms for researched devices in privacy, security and trust perception papers
  • Figure 2: PRISMA Procedure used for the Systematic Literature Review on privacy, security and trust perceptions.
  • Figure 3: Number of papers over time with the most recent paper from June 2023
  • Figure 4: Author's affiliation by country for privacy, security and trust perception papers in percentage. Due to the different number of papers for Privacy & Security and Trust, papers can have multiple authors from varying countries.