Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Digital Marketing and Social Media
Abstract
Abstract—The increasing use of Artificial Intelligence (AI) in digital advertising has created a new opportunity for businesses while also raising ethical concerns related to privacy, transparency, authenticity, manipulation, and algorithmic bias. These ethical issues may influence how consumers perceive advertising credibility, develop trust, and make purchase decisions. This study conducts a systematic literature review (SLR) of 44 peer-reviewed articles published in Scopus between 2022 and 2026, using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework. The way ethical aspects in AI-generated advertising content shape perceived advertising credibility, consumer trust, and purchase behavior is examined through content analysis. The findings show that transparent AI disclosure, privacy protection, authenticity, fairness, and explainability improve the perceived credibility of AI-generated advertising. Higher credibility helps build trust because consumers tend to perceive the AI-generated advertising as believable, reliable, and useful, which positively influences purchase decisions. Conversely, inauthenticity, poor explainability, privacy concerns, and manipulation reduce credibility, weaken consumer trust, and lower the likelihood of a consumer's decision to purchase.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...
This work revisits schema linking when using the latest generation of large language models (LLMs) and finds empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones.
Karime Maamari, Fadhil Abubaker, Daniel Jaroslawicz et al.· arXiv.org· 109 citations· ⚡19
A novel threat is unveiled in which attackers steer the RAG system's response by injecting malicious passages into its knowledge base, enabling the attacker to steer the response without altering the user input or modifying the RAG weights.
Jiaqi Xue, Meng Zheng, Yebowen Hu et al.· arXiv.org· 109 citations· ⚡8
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