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Many companies, including Google, Amazon, and Apple, offer voice assistants as a convenient solution for answering general voice queries and accessing their services. These voice assistants have gained popularity and can be easily accessed through various smart devices such as smartphones, smart speakers, smartwatches, and an increasing array of other devices. However, this convenience comes with potential privacy risks. For instance, while companies vaguely mention in their privacy policies that they may use voice interactions for user profiling, it remains unclear to what extent this profiling occurs and whether voice interactions pose greater privacy risks compared to other interaction modalities. In this paper, we conduct 1171 experiments involving 24530 queries with different personas and interaction modalities during 20 months to characterize how the three most popular voice assistants profile their users. We analyze factors such as labels assigned to users, their accuracy, the time taken to assign these labels, differences between voice and web interactions, and the effectiveness of profiling remediation tools offered by each voice assistant. Our findings reveal that profiling can happen without interaction, can be incorrect and inconsistent at times, may take several days or weeks to change, and is affected by the interaction modality.
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PrISM-Q&A: Step-Aware Voice Assistant on a Smartwatch Enabled by Multimodal Procedure Tracking and Large Language Models
Voice assistants capable of answering user queries during various physical tasks have shown promise in guiding users through complex procedures. However, users often find it challenging to articulate their queries precisely, especially when unfamiliar with the specific terminologies required for machine-oriented tasks. We introduce PrISM-Q&A, a novel question-answering (Q&A) interaction termed step-aware Q&A, which enhances the functionality of voice assistants on smartwatches by incorporating Human Activity Recognition (HAR) and providing the system with user context. It continuously monitors user behavior during procedural tasks via audio and motion sensors on the watch and estimates which step the user is performing. When a question is posed, this contextual information is supplied to Large Language Models (LLMs) as part of the context used to generate a response, even in the case of inherently vague questions like What should I do next with this? Our studies confirmed that users preferred the convenience of our approach compared to existing voice assistants. Our real-time assistant represents the first Q&A system that provides contextually situated support during tasks without camera use, paving the way for the ubiquitous, intelligent assistant.
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- Award ID(s):
- 2406099
- PAR ID:
- 10633257
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 8
- Issue:
- 4
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 26
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3699759
