Gender-Based Heterogeneity in Youth Privacy-Protective Behavior for Smart Voice Assistants: Evidence from Multigroup PLS-SEM

• URL: http://arxiv.org/abs/2603.27117v1
• Date: Sat, 28 Mar 2026 04:11:05 GMT
• Title: Gender-Based Heterogeneity in Youth Privacy-Protective Behavior for Smart Voice Assistants: Evidence from Multigroup PLS-SEM
• Abstract summary: This paper investigates how gender shapes privacy decision-making in youth smart voice assistant (SVA) ecosystems.<n>Using survey data from 469 Canadian youths aged 16-24, we compare males and females across five privacy constructs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates how gender shapes privacy decision-making in youth smart voice assistant (SVA) ecosystems. Using survey data from 469 Canadian youths aged 16-24, we apply multigroup Partial Least Squares Structural Equation Modeling to compare males (N=241) and females (N=174) (total N = 415) across five privacy constructs: Perceived Privacy Risks (PPR), Perceived Privacy Benefits (PPBf), Algorithmic Transparency and Trust (ATT), Privacy Self-Efficacy (PSE), and Privacy Protective Behavior (PPB). Results provide exploratory evidence of gender heterogeneity in selected pathways. The direct effect of PPR on PPB is stronger for males (Male: \b{eta} = 0.424; Female: \b{eta} = 0.233; p < 0.1), while the indirect effect of ATT on PPB via PSE is stronger for females (Female: \b{eta} = 0.229; Male: \b{eta} = 0.132; p < 0.1). Descriptive analysis of non-binary (N=15) and prefer-not-to-say participants (N=39) shows lower trust and higher perceived risk than the binary groups, motivating future work with adequately powered gender-diverse samples. Overall, the findings provide exploratory evidence that gender may moderate key privacy pathways, supporting more responsive transparency and control interventions for youth SVA use.

Related papers

• Negotiating Privacy with Smart Voice Assistants: Risk-Benefit and Control-Acceptance Tensions [0.0]
  This study introduces a negotiation-based framework for understanding youth privacy decision-making with SVAs.<n>We examine the distribution of two composite indices: the Risk-Benefit Tension Index (RBTI) and the Control-Acceptance Tension Index (CATI) using survey data from 469 Canadian youth aged 16-24.
  arXiv  Detail & Related papers  (2026-04-04T06:35:59Z)
• From Framework to Practice: Youth Negotiations of Privacy with Smart Voice Assistants Through the PEA-AI Lens [0.0]
  This study applies the Privacy-Ethics Alignment in AI framework to examine how youth perceive and negotiate privacy within SVAs.<n>We measured five privacy constructs: Perceived Privacy Risk (PPR), Perceived Privacy Benefits (PPBf), Algorithmic Transparency and Trust (ATT), Privacy Self-Efficacy (PSE), and Privacy-Protective Behavior (PPB)
  arXiv  Detail & Related papers  (2026-02-14T03:03:57Z)
• Privacy by Voice: Modeling Youth Privacy-Protective Behavior in Smart Voice Assistants [0.0]
  This study investigates how Canadian youth negotiate privacy with Smart Voice Assistants (SVAs)<n>It develops and tests a structural model grounded in five key constructs: perceived privacy risks (PPR), perceived benefits (PPBf), algorithmic transparency and trust (ATT), privacy self-efficacy (PSE) and privacy-protective behaviors (PPB)
  arXiv  Detail & Related papers  (2026-02-09T05:56:51Z)
• NLP Privacy Risk Identification in Social Media (NLP-PRISM): A Survey [9.47737368469032]
  We propose the NLP Privacy Risk Identification in Social Media framework, which evaluates vulnerabilities across six dimensions.<n>Our analysis shows that transformer models achieve F1-scores ranging from 0.58-0.84, but incur a 1% - 23% drop under privacy-preserving fine-tuning.<n>We advocate for stronger anonymization, privacy-aware learning, and fairness-driven training to enable ethical NLP in social media contexts.
  arXiv  Detail & Related papers  (2026-01-26T21:09:48Z)
• Convenience vs. Control: A Qualitative Study of Youth Privacy with Smart Voice Assistants [0.0]
  We investigate how perceived privacy risks (PPR) and benefits (PPBf) intersect with algorithmic transparency and trust (ATT) and privacy self-language overload (PSE)<n>Our analysis reveals that policy, fragmented settings, and unclear data retention undermine self-efficacy and discourage protective actions.<n>We derive actionable design guidance for SVAs, including a unified privacy hub, plain-language "data nutrition" labels, clear retention defaults, and device-conditional micro-tutorials.
  arXiv  Detail & Related papers  (2026-01-07T21:15:29Z)
• BioPro: On Difference-Aware Gender Fairness for Vision-Language Models [50.40913324046528]
  Vision-Language Models (VLMs) inherit significant social biases from their training data, notably in gender representation.<n>We build upon recent advances in difference-aware fairness for text-only models to formalize the problem of difference-aware gender fairness for image captioning and text-to-image generation.<n>We propose BioPro, which aims to mitigate unwanted bias in neutral contexts while preserving valid distinctions in explicit ones.
  arXiv  Detail & Related papers  (2025-11-30T09:33:09Z)
• Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation [18.123459468576648]
  We study whether federated recommendation systems can achieve group fairness under stringent privacy constraints.<n>We propose Privacy-Preserving Orthogonal Aggregation (PPOA), which employs the secure aggregation scheme and quantization technique.<n> Experimental results show PPOA enhances recommendation effectiveness for both females and males by up to 8.25% and 6.36%, respectively.
  arXiv  Detail & Related papers  (2024-11-29T13:12:11Z)
• The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models [91.86718720024825]
  We center transgender, nonbinary, and other gender-diverse identities to investigate how alignment procedures interact with pre-existing gender-diverse bias.<n>Our findings reveal that DPO-aligned models are particularly sensitive to supervised finetuning.<n>We conclude with recommendations tailored to DPO and broader alignment practices.
  arXiv  Detail & Related papers  (2024-11-06T06:50:50Z)
• GenderBias-\emph{VL}: Benchmarking Gender Bias in Vision Language Models via Counterfactual Probing [72.0343083866144]
  This paper introduces the GenderBias-emphVL benchmark to evaluate occupation-related gender bias in Large Vision-Language Models. Using our benchmark, we extensively evaluate 15 commonly used open-source LVLMs and state-of-the-art commercial APIs. Our findings reveal widespread gender biases in existing LVLMs.
  arXiv  Detail & Related papers  (2024-06-30T05:55:15Z)
• Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation [64.65911758042914]
  We investigate how seniority impacts the degree of gender bias exhibited in pretrained neural generation models. Our results show that GPT-2 amplifies bias by considering women as junior and men as senior more often than the ground truth in both domains. These results suggest that NLP applications built using GPT-2 may harm women in professional capacities.
  arXiv  Detail & Related papers  (2022-05-19T20:05:02Z)
• Balancing Biases and Preserving Privacy on Balanced Faces in the Wild [50.915684171879036]
  There are demographic biases present in current facial recognition (FR) models. We introduce our Balanced Faces in the Wild dataset to measure these biases across different ethnic and gender subgroups. We find that relying on a single score threshold to differentiate between genuine and imposters sample pairs leads to suboptimal results. We propose a novel domain adaptation learning scheme that uses facial features extracted from state-of-the-art neural networks.
  arXiv  Detail & Related papers  (2021-03-16T15:05:49Z)
• Gender Stereotype Reinforcement: Measuring the Gender Bias Conveyed by Ranking Algorithms [68.85295025020942]
  We propose the Gender Stereotype Reinforcement (GSR) measure, which quantifies the tendency of a Search Engines to support gender stereotypes. GSR is the first specifically tailored measure for Information Retrieval, capable of quantifying representational harms.
  arXiv  Detail & Related papers  (2020-09-02T20:45:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.