Artificial Intelligence in Digital Marketing: Towards an Analytical Framework for Revealing and Mitigating Bias

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript addresses a critical and timely topic: the role of artificial intelligence in digital marketing, with a focus on bias mitigation. This topic is increasingly relevant as AI tools become more pervasive in marketing. Although the problem under study is intriguing, I have the following concerns that must be addressed. Consequently, the paper requires significant revision.

(1) The abstract effectively summarizes the research. However, it could briefly highlight the practical implications of the proposed framework to better appeal to practitioners.

(2) The review provides a solid foundation, but it would benefit from deeper exploration of bias mitigation strategies in industries outside of marketing, to show how cross-disciplinary learnings could be applied.

(3) The mono-method qualitative approach is appropriate, but the small sample size (6 interviewees from one organization) limits generalizability. This limitation is acknowledged but should be discussed more critically.

 (4) The thematic analysis is thorough, but there is room for deeper integration of the primary research findings with the literature. For example, the discussion on Eurocentric marketing practices could be tied more explicitly to the proposed framework.

(5) The framework is well-conceived and visually clear. However, its practical implementation steps could be elaborated further, particularly for smaller organizations that may lack resources for extensive bias mitigation efforts.

(6) The limitations section is transparent, but the scope for future research could be expanded to include quantitative validation of the framework.

(7) A few grammatical errors and formatting inconsistencies (e.g., line spacing in Figure captions) should be addressed.

(8) Ensure all figures are legible, with consistent design elements.

(9) The introduction effectively outlines the significance of addressing bias in AI-driven digital marketing. However, it could be further strengthened by incorporating more recent references, particularly from the past 2–3 years, to ensure the discussion reflects the latest advancements in the field. Specifically:

Artificial intelligence on digital marketing-An overview

Artificial intelligence in digital marketing: Insights from a comprehensive review

A digital twin-based framework for damage detection of a floating wind turbine structure under various loading conditions based on deep learning approach

Comments on the Quality of English Language

The English could be improved to more clearly express the research.

Author Response

Please see uploaded file.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

In this paper, the authors focus on the role of AI in digital marketing, focusing on biases in AI coding, prompting, and deployment. The authors propose an analytical framework to reveal and mitigate these biases, using insights from a literature review and interviews with marketing professionals at a global software vendor. The study mainly emphasizes the practical and ethical implications of integrating AI technologies into marketing strategies.

The paper explores an important and timely topic by examining the integration of AI in digital marketing and addressing bias challenges. The combination of a systematic literature review and qualitative interviews provides a solid foundation for the proposed framework. While the study is thoughtful and well-structured, some areas could be expanded for broader applicability. Overall, the paper offers a meaningful contribution and should be considered for publication after a review.

Some aspects that, in my opinion, are needed to be addressed are the following:

• The introduction lacks specificity regarding the unique contribution of this study compared to existing research on AI bias in marketing. The authors should explicitly state how their framework differs from or advances prior work.
• The context provided for bias issues in AI is broad and vague. It would benefit from more focused examples relevant to digital marketing, particularly in coding and prompting. Also, novel contexts that integrate marketing aspects, such as eSports and data analytics could be discussed. For this matter, the authors could consider citing recent references such as [10.1016/j.ipm.2023.103516]
• The systematic approach (PRISMA) is described, but the inclusion/exclusion criteria for literature seems to not be adequately justified. Clarifying these would improve methodological transparency.
• A discussion of why alternative or mixed methods were not considered could enhance the proposed contribution.
• The participant sample is small and also seems homogenous due to the fact that it regards one company. The authors should discuss how this impacts the applicability of their findings.
• The results emphasize qualitative insights but lack quantitative or structured analysis of biases. For instance, frequency of themes could be a nice insight to look at. The authors should consider adding a statistical overview (even limited) of their findings.
• Finally, there are some typos and unusual sentence constructions, I suggest the authors to carefully read the paper and fix those.

 

Author Response

Please see uploaded file.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

Let me evaluate the article on several parameters.

 

Article language:

clear and generally native, easy to read

 

Article topic:

focus on studying the influence of subjectivity (bias) on content in marketing using generative and traditional AI networks

 

Research methods:

literature review, survey of a small number of respondents

 

General impressions:

Overall, the article has collected interesting facts. Although it is already clear that generative networks work on prompts, and they are subjective, this is confirmed in the study. In the reviewer's opinion, the topic of gender imbalances is far-fetched, after all, this can all be solved by adding the necessary commands to the prompt ("I want to be gender neutral" or "give me both male and female points of view") if this is really needed by a specific organization.

 

Quality of the figures:

overall it is not very good, almost all the diagrams should be redrawn with high DPI, the authors are advised to open their document on a good quality screen like a Mac and see that half of the text there is blurry. Also, tables should be inserted as tables and not as figures.

 

References:

a large number of Internet links does not indicate a very good representation of the topic in the academic community, although this is probably not a disadvantage.

 

Results:

it is not very clear how the current results can be used by the scientific community.

 

Overall, this all seems worthy of publication, but the conclusions are not very deep.

Author Response

Please see uploaded file.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The authors successfully addressed my comments.