Personalization with Principles: Ethical AI Strategies inAdvertising - A Master’s Thesis
Abstract
My master’s thesis delves into the ethical dilemmas and widening trust gap emerging between businesses and consumers in the realm of AI-driven advertising. Through a mixed-methods approach, combining surveys with expert interviews, we show that business professionals trust AI not merely due to familiarity with it but because they actively participate in its adoption, witnessing first-hand the efficiency and benefits it brings to their operations. Consumers, meanwhile, remain more skeptical. With many of them lacking a deep understanding of how AI systems function and how their data is handled, they express concerns about the lack of transparency surrounding these processes. This research highlights a growing need for more transparent practices, stronger consumer control, and more robust ethical frameworks that can bridge this trust gap. It calls for a collaborative governance model that pulls businesses, consumers, regulators, and AI experts together to build trustworthy systems and approaches.
Keywords: AI-driven Advertising, Personalization, Consumer Trust, Data Privacy, Stakeholder Collaboration, AI Ethics
Executive Summary
Problem
The advertising industry has long been criticized for its manipulative tactics and promotion of unchecked consumerism, and with the rise of AI-driven personalization, these concerns have only deepened. While consumers worry about the ethical consequences of AI, businesses view it as a golden ticket to boost sales and efficiency. This creates a significant tension between ethics and profitability, one that proves hard to resolve given the differing priorities of both parties. On one side, companies eager to enhance their marketing through AI risk alienating customers if they fail to address ethical concerns. On the other side, the complexity of AI systems, which remain opaque to most consumers, only heightens their suspicions and discomfort.
This thesis aims to explore this gap by examining the contrasting viewpoints of business professionals and consumers. It seeks to find actionable insights that balance the need for business efficiency with ethical responsibility. Understanding these differing perspectives is crucial for developing AI-driven advertising strategies that are both effective and meet consumer expectations.
Method
Our research followed a mixed-methods approach, beginning with a literature review to outline the key debates and concerns in the field. We used a Convergent Parallel design to ensure both breadth and depth in our findings, combining survey data and expert interviews.
The quantitative component involved surveys targeting two groups: 100 business professionals and 100 consumers. The surveys aimed to highlight patterns and differences in their attitudes towards AI-driven personalization, focusing on ethical concerns, trust in companies, and comfort with data use. Data analysis included both descriptive and inferential statistics, using non-parametric tests like the Mann-Whitney U test to account for non-normality in the data. Additionally, we performed Spearman’s rank-order correlation to dive deeper into the reasons behind the differing perspectives.
For the qualitative aspect, we conducted in-depth, semi-structured interviews with three experts from business, policy, and academia. These interviews offered practical, regulatory, and ethical insights into AI’s role in advertising, complementing the survey findings. By integrating quantitative surveys with qualitative interviews, our methodology provided a well-rounded analysis of both broader trends and more nuanced viewpoints.
Findings
Our research identified a significant trust gap between business professionals and consumers. Business professionals tend to trust AI due to their familiarity with the technology as well as their direct involvement in its implementation, which gives them a sense of control and confidence in its benefits. They are also more aware of the operational efficiencies AI provides and are often more exposed to internal safeguards within their organizations. On the other hand, consumers, who have less insight into how AI systems function and how their data is managed, tend to be more cautious and skeptical. Their concerns stem from a lack of transparency, fear of data misuse, and concerns around the broader ethical implications of AI, all of which contribute to their skepticism and discomfort with AI-driven advertising.
To address these challenges, we recommend the development of a Multistakeholder Governance Model that incorporates inputs from businesses, consumers, regulators, and independent experts. This multi-stakeholder approach would assist in balancing technological innovation with ethical responsibility, thus helping to build consumer trust in AI-driven advertising.
Our findings also highlight that the ethical challenges posed by AI in advertising are not new but rather the magnification of existing issues such as privacy concerns and the misuse of consumer data. As AI technologies evolve, businesses must take proactive steps to address these ethical challenges and align their practices with consumer expectations, since these concerns will become more prevalent and noticeable with the use of AI. Therefore, this thesis aims to spark further discussions about the future of AI in advertising. It encourages businesses to prioritize transparency, collaboration, awareness building, and consumer autonomy in their AI strategies. By addressing these core issues, companies can close the trust gap, ensuring that AI-driven innovations benefit both businesses and consumers in a fair and ethical manner.