JISARA

Abstract: It has been established by research and marketing firms that advertising on social media is due to its reach, cost effectiveness and range of media formats appealing to a range of target audiences. However, a campaign needs to be properly designed to reach the intended audiences and therefore be value for money. Therefore, the focus of this research is to evaluate the effectiveness of AI-related advertising campaigns, particularly in reaching their intended audiences, such as individuals with a declared interest in AI. This study addresses the under-explored area of how effectively these campaigns target and engage their intended demographic groups, revealing gaps and opportunities in current advertising strategies. A dataset of 5,528 AI-related advertisements was analyzed to evaluate the parameters of targeting precision, demographic distribution, and geographical variations. The analysis aimed to identify discrepancies in ad reach and effectiveness across different demographic segments and various geographical regions. The findings show a significant discrepancy between overall ad volume and targeted reach, with only 12.6% of ads directly targeting individuals interested in AI. Our findings reveal a distinct demographic skew in AI ad exposure, favoring males and the 25-34 age group. This gender disparity is particularly pronounced when targeting AI enthusiasts, raising concerns about potential bias in ad delivery. Significant geographical variations in ad performance were also identified, underscoring the importance of regional context in advertising strategies. The implications of these findings for advertisers are clear: more precise targeting is needed to engage AI-interested audiences effectively, algorithmic biases must be addressed to ensure equitable ad delivery, and contextual factors should be considered to optimize campaign strategies.

Download this article: JISARA - V18 N4 Page 56.pdf

Recommended Citation: Singha Roy, S., Linden, T., (2025). AI-Related Advertising on Facebook: Addressing Bias, Targeting Challenges and Regional Factors. Journal of Information Systems Applied Research and Analytics 18(4) pp 56-66. https://doi.org/10.62273/IJSS6362