Privacy concerns toward AI-based intelligent voice assistants in the workplace
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Although privacy issues have been widely examined in relation to workplace technology, this study/paper aims to provide a deeper understanding of privacy from a workplace perspective in the context of AI-based intelligent voice assistants. Design/methodology/approach Given the call for more qualitative-empirical studies to examine AI-based intelligent voice assistants, this study conducted 26 in-depth, semistructured interviews with a range of North American organizations across various sectors and industry types. Guided by a constructionist research paradigm and employing a thematic analysis approach, the study focuses on the subjective experiences and insights of participants regarding the use of digital assistants in the workplace. Findings While AI-based intelligent voice assistants can increase productivity and efficiency, the findings reveal that issues related to worker privacy are a significant area of concern. The perceived omnipresent nature of voice assistants fostered apprehensions regarding the listening to and recording of conversations, particularly personal information (information collection). Concerns were raised regarding information processing, specifically whether data was being used for its intended purpose. Fears were also raised about the unintentional dissemination of information, often due to concerns associated with technical glitches. The findings also reveal the invasive nature of digital assistants and their potential to disrupt an individual’s daily routine and personal space. Originality/value Drawing on Solove’s theoretical underpinnings, particularly the work on privacy, this paper offers a fresh perspective on understanding privacy concerns surrounding AI-based intelligent voice assistants in the workplace.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it