Human-AI Alignment in Ad Targeting
Addressing Misestimation for Vulnerable Groups
Bibliographic Data
| ID | 21422883 |
|---|---|
| Authors | Yang Zhang (0000-0001-6777-3487, Tianjin University, corresponding author), Shouxin Zhang (0009-0006-9093-414X, Tianjin University) |
| Year | 2025 |
| Volume | 54 |
| Issue | 2 |
| Pages | 196-212 |
| Publication date | 2025-03-15 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Advertising (JOURNAL) |
| Journal identifiers | ISSN: 0091-3367 • E-ISSN: 1557-7805 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00913367.2025.2465381 |
| OpenAlex | W4409555024 |
| Language | EN |
| Citations received | 2 |
| References cited | 40 |
Users are playing an increasingly active role in receiving advertising information, making it essential to understand their assessment of algorithm from folk theory perspectives. In response to this need, we introduce the concept of “misestimation” to describe the gap between users’ perceptions of algorithmic systems and their actual operations. Focusing on the misestimation of targeting on Weibo, this research specifically focusing on female users’ overestimation/underestimation and the factors that contribute to these misestimations. Employing a mixed-method approach, the study conducted surveys and in-depth interviews with 25 participants. By identifying differences between overestimation and underestimation groups, the study developed a theoretical model of the themes influencing these perceptions: algorithm attitude, algorithm literacy, perceived surveillance, gender bias, and motivation to use platform. Methodologically, this study propose a mix-method to operationalize misestimation, which makes folk theory truly operationalizable. Theoretically, this article contributes a key concept for understanding human–machine alignment. Additionally, the findings advance the literature on uses and gratifications theory, algorithm attitude, and perceived surveillance theory by expanding them to human–machine alignment. By adjusting these influencing factors, this research offers suggestions to advertisers on Chinese platforms for how to improve AI service, in navigating personalized advertising content without falling into information cocoons or fostering distrust
Advertising · Business · Computer security · Internet privacy · Computer Science · Ethics and Social Impacts of AI · Marketing · Neuroethics, Human Enhancement, Biomedical Innovations · Psychology
The Coding Manual for Qualitative Researchers
Algorithmic bias
Public attitudes towards algorithmic personalization and use of personal data online
User Perceptions of Algorithmic Decisions in the Personalized AI System
Can Algorithm Knowledge Stop Women from Being Targeted by Algorithm Bias? The New Digital Divide on Weibo
Why people would (not) change their media use in response to perceived corporate surveillance
Why do people purchase virtual goods? A uses and gratification (U&G) theory perspective
Advancing Computational Advertising
Making sense of algorithmic profiling
To be or not to be algorithm aware
Surveillance, security, and AI as technological acceptance
The validation of the Perceived Surveillance Scale
A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use
Development and Validation of an Algorithm Literacy Scale for Internet Users
Vulnerability in a tracked society
Rise of Machine Agency
The algorithmic persuasion framework in online communication
Folk theories of algorithms
Algorithm dependency in platformized news use
Baking Gender Into Social Media Design
| Citations per year | 2 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |