Social media advertising for clinical studies: Ethical and data protection implications of online targeting

Abstract

Social media advertising has revolutionised the advertising world by providing data-driven targeting methods. One area where social media advertising is just gaining a foothold is in the recruitment of clinical study participants. Here, as everywhere, social media advertising promises more yield per money spent because the technology can better reach highly specialised groups. In this article, we point out severe societal risks posed by advertising for clinical studies on social media. We show that social media advertising for clinical studies in many cases violates the privacy of individual users (R1), creates collective privacy risks by helping platform companies train predictive models of medical information that can be applied to all their users (R2), exploits the weaknesses of existing guidelines in (biomedical) research ethics (R3) and is detrimental to the quality of (biomedical) research (R4). We argue that the well-intentioned promises, which are often associated with the use of social media advertising for clinical studies, are untenable from a balanced point of view. Consequently, we call for updates of research ethics guidelines and better regulation of Big Data and inferential analytics. We conclude that social media advertising – especially with vulnerable patient populations – is not suitable as a recruitment tool for clinical studies as long as the processing of (even anonymised) social media usage data and the training of predictive models by data analytics and artificial intelligence companies is not sufficiently regulated.

Artificial Intelligence in Advertising: Advancements, Challenges, and Ethical Considerations in Targeting, Personalization, Content Creation, and Ad Optimization

“…Machine Learning serves as the technological foundation for achieving this kind of Targeting (Choi & Lim, 2020;Thareja & Jain, 2019). Machine Learning plays an essential role in the application of AI technology in the advertising industry, especially for Targeting to identify target audiences (Helsloot et al, 2018;M€ uhlhoff & Willem, 2023). Studies by Choi and Lim (2020) and Helsloot et al (2018) explored the technical characteristics that machine learning should possess in the advertising domain and pointed out that machine learning algorithms can recognize consumer behavioral patterns and trends.…”

“…It controls the content, frequency, and timing of ad placements based on analyzing advertising performance data. This determines which ads and messages are most effective, helping advertisers optimize their campaigns to achieve maximum engagement and conversion rates (M€ uhlhoff & Willem, 2023). This approach is conducive to maximizing the Click Through Rate (CTR) and advertising campaign revenue (Nikolajeva & Teilans, 2021), offering advertisers a direct and efficient method to identify the most effective advertisements.…”

“…Specifically, AI is increasingly employed in advertising Targeting, Personalization, Content Creation, and Ad Optimization (Bhatt, 2021;Campbell, Plangger, Sands, Kietzmann, Bates, 2022;Jaiwant, 2023;Malthouse & Copulsky, 2023;Nikolajeva & Teilans, 2021). By analyzing consumer behavior, AI technology offers valuable insights that aid advertisers in formulating more effective strategies, this enhances the efficiency of advertisement information processing and decision-making (e.g., Malthouse & Copulsky, 2023;M€ uhlhoff & Willem, 2023), representing breakthroughs in the field. In the realm of advertising Targeting, various machine learning techniques can be employed to enhance targeted online advertising M€ uhlhoff & Willem, 2023), especially in optimizing target audience scope, significantly improving the ability to segment target users (Chandra et al, 2022;Choi & Lim, 2020).…”

“…The four key pillars of AI advertising based on Computational Advertising (Targeting, Personalization, Content Creation, and Ad Optimization) have attracted widespread attention from the academic community and have been extensively studied. For example, a group of researchers studied the domain of Targeting (Bateni et al, 2017;Choi & Lim, 2020;Helsloot et al, 2018;M€ uhlhoff & Willem, 2023;Theodoridis & Gkikas, 2019). Another group explored the domain of Personalization (Bansal & Gupta, 2023;Campbell, Plangger, Sands, Kietzmann, Bates, 2022;Chandra et al, 2022;Nikolajeva & Teilans, 2021).…”

“…Incorporating Generative AI into advertising campaigns and content creation processes can enhance creative quality and ad impact. Furthermore, with Deep Learning and Reinforcement Learning techniques, Ad Optimization tailors advertisements more closely to users' actual needs, enhancing advertisement efficacy and user purchase conversion rates (M€ uhlhoff & Willem, 2023;Nikolajeva & Teilans, 2021;X. Zhang et al, 2017).…”