Privacy concerns toward AI-based intelligent voice assistants in the workplace
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".