The AI Invisibility Effect: Understanding Human-AI Interaction When Users Don't Recognize Artificial Intelligence
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914230224027648 |
|---|---|
| author | Kraishan, Obada |
| author_facet | Kraishan, Obada |
| contents | The fast integration of artificial intelligence into mobile applications has completely changed the digital landscape; however, the impact of this change on user perception of AI features remains poorly understood. This large-scale analysis examined 1,484,633 mobile application reviews across 422 applications (200 AI-featuring, 222 control) from iOS App Store and Google Play Store. By employing sentiment classification, topic modeling, and concern-benefit categorization, we identified a major disconnect: only 11.9% of reviews mentioned AI, even though 47.4% of applications featured AI capabilities. AI-featuring applications received significantly lower ratings than traditional applications (d = 0.40); however, hierarchical regression revealed a hidden pattern - the negative relationship reversed after controlling for AI mentions and review characteristics (b = 0.405, p < .001). Privacy dominated user concerns (34.8% of concern-expressing reviews), while efficiency represented the primary benefit (42.3%). Effects varied greatly by category, from positive for Assistant applications (d = 0.55) to negative for Entertainment (d = -0.23). These findings suggest that AI features often operate below user awareness thresholds, and it is the explicit recognition of AI, rather than its mere presence, that drives negative evaluations. This challenges basic assumptions about technology acceptance in AI systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00579 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The AI Invisibility Effect: Understanding Human-AI Interaction When Users Don't Recognize Artificial Intelligence Kraishan, Obada Human-Computer Interaction H.5.2; H.1.2; J.4 The fast integration of artificial intelligence into mobile applications has completely changed the digital landscape; however, the impact of this change on user perception of AI features remains poorly understood. This large-scale analysis examined 1,484,633 mobile application reviews across 422 applications (200 AI-featuring, 222 control) from iOS App Store and Google Play Store. By employing sentiment classification, topic modeling, and concern-benefit categorization, we identified a major disconnect: only 11.9% of reviews mentioned AI, even though 47.4% of applications featured AI capabilities. AI-featuring applications received significantly lower ratings than traditional applications (d = 0.40); however, hierarchical regression revealed a hidden pattern - the negative relationship reversed after controlling for AI mentions and review characteristics (b = 0.405, p < .001). Privacy dominated user concerns (34.8% of concern-expressing reviews), while efficiency represented the primary benefit (42.3%). Effects varied greatly by category, from positive for Assistant applications (d = 0.55) to negative for Entertainment (d = -0.23). These findings suggest that AI features often operate below user awareness thresholds, and it is the explicit recognition of AI, rather than its mere presence, that drives negative evaluations. This challenges basic assumptions about technology acceptance in AI systems. |
| title | The AI Invisibility Effect: Understanding Human-AI Interaction When Users Don't Recognize Artificial Intelligence |
| topic | Human-Computer Interaction H.5.2; H.1.2; J.4 |
| url | https://arxiv.org/abs/2601.00579 |