Dark sides of artificial intelligence: The dangers of automated decision‐making in search engine advertising

Schultz, Carsten D.; Koch, Christian; Olbrich, Rainer

doi:10.1002/asi.24798

Cited by 6 publications

(2 citation statements)

References 68 publications

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“…In "Dark sides of artificial intelligence: The dangers of automated decision-making in search engine advertising," by Carsten D. Schultz et al (2024), the authors explore the pressing question of what the potential dangers of automated bidding by search engines are, and how they can effectively be countered? Using empirical data from a real case, the authors show that the integration of artificial intelligence can indeed lead to reduced advertisement performance.…”

Section: An Overview Of Contributions In This Issuementioning

confidence: 99%

JASIST Special Issue Editorial: Re‐orienting search engine research in information science

Lewandowski,

Haider,

Sundin

2023

Asso for Info Science & Tech

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General web search engines constitute a fundamental part of today's information infrastructure. They have made it possible to find almost anything on the internet, usually by typing a few keywords into an empty search box, but increasingly also by voice recognition. In fact, search engines are now so integrated into devices and practices that they mostly go unnoticed. They have come to shape our lives as well as the society and culture we live in, and our lives and society also affect these information resources in their turn. We use them in school, for work, in planning our holidays, when searching for products and services and in politics-just to name a few important areas.Given the centrality of general web search engines in how and what we know, and in whose products, services and messages get noticed, it is no surprise that content providers are doing everything they can, using various types of search engine optimization (SEO) and marketing (SEM), to achieve the highest possible rank and thus visibility on search engine result pages (SERP). When we talk about the uses of search engines, it is important to remember that there are other users than the ones we usually think of. Besides so-called end users or searchers, those who want to be found (content producers) are also users of a search engine, but also there are also those who do not want to be found.The search engine landscape is dominated by a few corporations that have divided the global market more or less along geopolitical lines: broadly speaking, Baidu in China, Yandex in Russia and Google in much of the Western world. Microsoft's Bing is often mentioned as the most important competitor to Google, but this search engine has failed to gain significant market shares. In the United States, Bing has a market share of about 6%, and 3% in Europe (Statcounter, 2023). More competition between search engines is often called for, but this is extremely difficult to achieve within the current political and economic system.

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Section: An Overview Of Contributions In This Issuementioning

confidence: 99%

JASIST Special Issue Editorial: Re‐orienting search engine research in information science

Lewandowski,

Haider,

Sundin

2023

Asso for Info Science & Tech

View full text Add to dashboard Cite

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“…Schultz et al critically highlighted the growing dependence on AI in enhancing automated bidding strategies in search engine advertising [51]. Their research showcased concerns over the lack of transparency in AI-powered systems, emphasizing the potential pitfalls and challenges advertisers face, especially when they are uninformed about the data driving these decisions.…”

Section: Digital Advertising Clustermentioning

confidence: 99%

Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review

Ziakis,

Vlachopoulou

2023

Information

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Artificial intelligence (AI) has rapidly emerged as a transformative force in multiple sectors, with digital marketing being a prominent beneficiary. As AI technologies continue to advance, their potential to reshape the digital marketing landscape becomes ever more apparent, leading to profound implications for businesses and their digital outreach strategies. This research seeks to answer the pivotal question: “How could AI applications be leveraged to optimize digital marketing strategies”? Drawing from a systematic literature review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, this study has identified 211 pertinent articles. Through a comprehensive bibliometric analysis, the findings were categorized into distinct clusters, namely: AI/ML (Machine Learning) Algorithms, Social Media, Consumer Behavior, E-Commerce, Digital Advertising, Budget Optimization, and Competitive Strategies. Each cluster offers insights into how AI applications can be harnessed to augment digital marketing efforts. The conclusion synthesizes key findings and suggests avenues for future exploration in this dynamic intersection of AI and digital marketing. This research contributes to the field by providing a comprehensive bibliometric analysis of AI in digital marketing, identifying key trends, challenges, and future directions. Our systematic approach offers valuable insights for businesses and researchers alike, enhancing the understanding of AI’s evolving role in digital marketing strategies.

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Dynamic algorithmic awareness based on FAT evaluation: Heuristic intervention and multidimensional prediction

Liu,

Wu,

Sun

et al. 2024

Asso for Info Science & Tech

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As the widespread use of algorithms and artificial intelligence (AI) technologies, understanding the interaction process of human–algorithm interaction becomes increasingly crucial. From the human perspective, algorithmic awareness is recognized as a significant factor influencing how users evaluate algorithms and engage with them. In this study, a formative study identified four dimensions of algorithmic awareness: conceptions awareness (AC), data awareness (AD), functions awareness (AF), and risks awareness (AR). Subsequently, we implemented a heuristic intervention and collected data on users' algorithmic awareness and FAT (fairness, accountability, and transparency) evaluation in both pre‐test and post‐test stages (N = 622). We verified the dynamics of algorithmic awareness and FAT evaluation through fuzzy clustering and identified three patterns of FAT evaluation changes: “Stable high rating pattern,” “Variable medium rating pattern,” and “Unstable low rating pattern.” Using the clustering results and FAT evaluation scores, we trained classification models to predict different dimensions of algorithmic awareness by applying different machine learning techniques, namely Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and XGBoost (XGB). Comparatively, experimental results show that the SVM algorithm accomplishes the task of predicting the four dimensions of algorithmic awareness with better results and interpretability. Its F1 scores are 0.6377, 0.6780, 0.6747, and 0.75. These findings hold great potential for informing human‐centered algorithmic practices and HCI design.

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Dark sides of artificial intelligence: The dangers of automated decision‐making in search engine advertising

Cited by 6 publications

References 68 publications

JASIST Special Issue Editorial: Re‐orienting search engine research in information science

JASIST Special Issue Editorial: Re‐orienting search engine research in information science

Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review

Dynamic algorithmic awareness based on FAT evaluation: Heuristic intervention and multidimensional prediction

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