Towards Equitable AI: Detecting Bias in Using Large Language Models for Marketing
The recent advances in large language models (LLMs) have revolutionized industries such as finance, marketing, and customer service by enabling sophisticated natural language processing tasks. However, the broad adoption of LLMs brings significant challenges, particularly in the form of social biases that can be embedded within their outputs. Biases related to gender, age, and other sensitive attributes can lead to unfair treatment, raising ethical concerns and risking both company reputation and customer trust. This study examined bias in finance-related marketing slogans generated by LLMs (i.e., ChatGPT) by prompting tailored ads targeting five demographic categories: gender, marital status, age, income level, and education level. A total of 1,700 slogans were generated for 17 unique demographic groups, and key terms were categorized into four thematic groups: empowerment, financial, benefits and features, and personalization. Bias was systematically assessed using relative bias calculations and statistically tested with the Kolmogorov-Smirnov (KS) test against general slogans generated for any individual. Results revealed that marketing slogans are not neutral; rather, they emphasize different themes based on demographic factors. Women, younger individuals, low-income earners, and those with lower education levels receive more distinct messaging compared to older, higher-income, and highly educated individuals. This underscores the need to consider demographic-based biases in AI-generated marketing strategies and their broader societal implications. The findings of this study provide a roadmap for developing more equitable AI systems, highlighting the need for ongoing bias detection and mitigation efforts in LLMs.
Code (0)
등록된 구현이 없습니다.
Tasks
Bias DetectionMarketingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Detecting Linguistic Bias in Government Documents Using Large language Models
This paper addresses the critical need for detecting bias in government documents, an underexplored area with significant implications for governance. Existing methodologies often overlook the unique context and far-reac…
Bias DetectionCross-Language Bias Examination in Large Language Models
This study introduces an innovative multilingual bias evaluation framework for assessing bias in Large Language Models, combining explicit bias assessment through the BBQ benchmark with implicit bias measurement using a …
Semi-automatic Detection of Cross-lingual Marketing Blunders based on Pragmatic Label Propagation in Wiktionary
We introduce the task of detecting cross-lingual marketing blunders, which occur if a trade name resembles an inappropriate or negatively connotated word in a target language. To this end, we suggest a formal task defini…
MarketingSESGO: Spanish Evaluation of Stereotypical Generative Outputs
This paper addresses the critical gap in evaluating bias in multilingual Large Language Models (LLMs), with a specific focus on Spanish language within culturally-aware Latin American contexts. Despite widespread global …
Analyzing Language Bias Between French and English in Conventional Multilingual Sentiment Analysis Models
Inspired by the 'Bias Considerations in Bilingual Natural Language Processing' report by Statistics Canada, this study delves into potential biases in multilingual sentiment analysis between English and French. Given a 5…
Multilingual NLPSentiment AnalysisSentiment Classification