Advertising benefits from ethical Artificial Intelligence algorithmic purchase decision pathways
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Abstract Artificial intelligence (AI) has dramatically changed the way organizations communicate, understand, and interact with their potential consumers. In the context of this trend, the ethical considerations of advertising when applying AI should be the core question for marketers. This paper discusses six dominant algorithmic purchase decision pathways that align with ethical philosophies for online customers when buying a product/goods. The six ethical positions include: ethical egoism, deontology (i.e., rule-based), relativist, utilitarianism, virtue ethics, and ethics of care (i.e., stakeholders’ perspective). Furthermore, this paper launches an “intelligent advertising” AI theme by examining its present and future as well as identifying the key phases of intelligent advertising. Several research questions are offered to guide future research on intelligent advertising to benefit ethical AI decision-making. Finally, several areas that can be widely applied to ethical intelligent advertising are suggested for future research.
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AI fairness is used for ad targeting to avoid promoting discriminatory or biased content to users based on their attributes.
“To address the issue above, AI fairness is used for ad targeting to avoid promoting discriminatory or biased content to users based on their attributes [293].”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [293]
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- Rodgers, W., & McFarlin, T. G. (2016). Decision making for personal investment: Real estate financing. Springer.
- Solomon, M. R. (2006). Consumer behaviour: A European perspective. Financial Times/Prentice Hall.
- Wang, Y., & Yu, C. (2017). Social interaction-based consumer deci- sion-making model in social commerce: The role of word of mouth and observational learning. International Journal of Infor- mation Management, 37, 179-189.
Cited by
Sorted by citations
AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications
Chen, P., Wu, L., & Wang, L. (2023) · Applied Sciences · doi:10.3390/app131810258 · Cited by 155 · see citing works
Basu, R., Aktar, M. N., & Kumar, S. (2025) · Journal of Marketing Analytics · doi:10.1057/s41270-024-00355-6 · Cited by 35 · see citing works
- Authors
- Rodgers, W., & Nguyen, T.
- Affiliations
- The University of Texas at El Paso (US)
- University of Hull (GB)
- Corresponding author
- Rodgers, Waymond
- Venue
- Journal of Business Ethics
- Published
- 2022-02-12
- Volume
- 178
- Issue
- 4
- Pages
- 1043-1061
- ISSN
- 0167-4544 (ISSN-L)
- 1573-0697
- OpenAlex
- W4211090457
- Cited by
- 137
- References
- 122
- Citations by year
- 2026: 38 · 2025: 35 · 2024: 33 · 2023: 21 · 2022: 10
- FWCI
- 28.68
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Business Ethics (publishedVersion, cc-by)
- Repository@Hull (Worktribe) (University of Hull) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Greater South Information System
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Information Systems and Management › Ethics in Business and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- ethical artificial intelligence, online consumer behavior, ethical egoism, deontology, relativism, utilitarianism, virtue ethics, care ethics, artificial intelligence in marketing, ethical decision-making
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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