Robots and Code
Authors: Krause Till 
Published: November 2024

Introduction
Artificial intelligence (AI) and its applications, such as large language models and generative AI, have become some of the most hotly debated technologies of our time. While they are often seen as harbingers of progress, they also evoke fear and uncertainty regarding their potential societal impacts.
Mass media, especially journalism, play a pivotal role in shaping public opinion on these technologies. In democratic societies, journalism is tasked with educating and informing the public, contributing to collective understanding and opinion formation. Brause et al. state that “media coverage of Al technologies is an important indicator of the central issues, actors, frames and evaluations attached to technology, and a critical arena where stakeholders negotiate future pathways for Al and its role in societies” (277).
However, how key technologies like AI are visually represented in editorial media remains an underexplored topic in current research. Which photos and/or illustrations are used to accompany news articles about this technology and its impact on society? This is of high importance to the overall understanding of the media representation of AI—a question at the intersection of visual studies, cultural studies, and communication studies. Previous research has shown that how new technologies are depicted (and not only how they are described) can significantly shape public discourse at multiple levels (Zeller et al.; Kelly).
For instance, studies of nanotechnology, a topic whose societal debate parallels some aspects of the current discourse about AI, reveal that media stories about these debates are often illustrated with rather stereotypical elements. Lösch notes that they are “frequently illustrated in mass media publications with futuristic science-fiction images” (255). This trend—depicting real technologies and their (as yet uncertain) societal consequences using visual tropes from science fiction, such as robots, androids, code, and futuristic models—can also be observed in recent discussions around AI, particularly in journalistic descriptions of the potential dangers posed by AI systems to society and humanity.
This article aims to examine how selected articles from German quality media (a definition of the term can be found in Meier and in Arnold), published between January and September 2024, visually illustrate the topic of “AI risks and dangers”. Rather than analysing the content of these articles, the focus will be on their visual accompaniment, exploring how images are used to shape public understanding of AI risks, often with a highly artificial appeal and Anmutung (impression).
The Representation of Technology in German Mass Media
Germany is sometimes perceived as a country that approaches digital technologies with caution, particularly regarding data protection and privacy rights. This is also partly true for the debates around artificial intelligence. Köstler and Ossewaarde argue that “it is a discourse that is marked by a certain alarmism about the future of the German economy” (250). Digitalisation and automation are perceived ambivalently by the German population (Störk-Biber et al. 30). On one hand, there are expectations of increased convenience; on the other hand, these are countered by risks, primarily related to perceived susceptibility to disruptions and threats to individual autonomy (30). Germany has comprehensive data protection laws at both federal and state levels, covering public and private sectors alike. Germans show strong public interest in privacy, as seen in widespread opposition to data retention laws and a strong support of privacy, illustrated for example by the fact that “240,000 persons in Germany have opted out of Google Street View“ (Schwarz 289). Researchers sometimes attribute this rather sceptical attitude towards digital technologies to Germany’s historical experiences with authoritarian regimes, describing a “climate of fundamental mistrust of the state, its security agencies and the new possibilities of data collection” (Knorre 17). This mistrust has resulted in a rather negative view of Big Data applications in Germany, and perpetuates a “narrative of the machine taking on a life of its own, which is associated with the term artificial intelligence” (Knorre 29). However, it has also been noted that Germany “takes a middle position in terms of attitudes toward digitalization in a European comparison” (Störk-Biber et al. 30), but that “there are also growing concerns about pressures, risks, and dependencies, whose responsible management is expected not only from the government and associations but also from companies” (31).
This study does not conduct a textual framing analysis, which has already been extensively applied in other contexts, such as the portrayal of digitalisation processes in German media (Zeller et al.). Instead, it focusses on visual communication and how it contributes to popular perceptions of the technology. That the selection of images plays an important role in journalism has been widely demonstrated (Sargent 705). Furthermore, it has been shown that articles without accompanying imagery get shared less often on social media, underlining the “importance of images in driving news consumption” (Keib 202).
Kelly highlights the significant role mass media play in the adoption of new technologies: “technology is rarely adopted on its merits alone; social forces both constrain and encourage adoption. Mass media is a significant influence, along with other social forces, in all stages of adoption” (34). By looking at photos and illustrations, this study seeks to examine the visual culture surrounding AI by showcasing a sample of such imagery and making it accessible for academic discourse, and thus, helping to validate the often expressed notion that “science fictional tropes abound in news coverage of A.I.” (Goode).
Given the pressures of time, budget, and labour that often accompany journalistic production, even within the realm of quality journalism, the selection of images should not be over-interpreted. Nonetheless, these visual representations provide valuable insight into the motifs frequently used to depict AI risks, offering a glimpse into how the media visually communicate complex technologies. Accordingly, the research question (RQ1) for this study was: What are the visual elements of journalistic coverage of the potential risks of artificial intelligence in German news media from January to September 2024?
Methodology
This study uses the method of qualitative content analysis (Mayring), which aims to “conceptualize the process of assigning categories to text passages as a qualitative-interpretive act, following content-analytical rules” (10). The qualitative content analysis method with inductive category development was chosen as the most suitable for this study. Since there is little existing research on the visual representation of AI risks in the media, the inductive method provides the flexibility needed to develop new categories from the image data, without being constrained by pre-existing theories or hypotheses.
To gain access to the data for further analysis, an Internet search was conducted using Google News with the keywords “KI Gefahr” (AI danger) and “KI Warnung” (AI warning) from 1 January to 1 September 2024, focussing exclusively on German-language media. Only articles from quality journalism outlets were considered, to exclude non-journalistic trade publications, advertising, and public relations-driven articles. Only visual accompaniments, such as illustrations and photos, were included in the analysis. Videos and preview images for videos were excluded. This timeframe was chosen because it represents an era where artificial intelligence has been established as a main interest after the initial hype surrounding the first publication of OpenAI’s ChatGPT Model in late 2022.
A cursory content analysis was conducted to ensure that only articles taking a critical stance on AI were included in the dataset, and no mislabelling took place. The final dataset consists of 21 images drawn from quality journalism sources, defined by their adherence to journalistic standards such as accuracy, independence, and balance, and came from the following outlets (in alphabetical order): Deutsche Welle, Deutschlandfunk, FAZ, Frankfurter Rundschau, Handelsblatt, NZZ, SRF, SWR, Tagesspiegel, WDR, Welt, ZDF.
The study uses QCAmap software, designed specifically for qualitative content analysis, to systematically code and structure the visual material. This approach ensures that the process of category development remains transparent and iterative, allowing for the emergence of unexpected patterns and visual trends within the media portrayals of AI risks.
Following the rules of qualitative content analysis, the categories were revised after coding 30% of the material, with minor adaptions in the categories being made. In total, 12 categories (RQ1-1 – RQ1-12)—some of them clustered into subcategories—emerged from the data.
Results
The qualitative content analysis of 21 illustrative images using QCAmap with inductive category formation revealed significant trends in the visual depiction of artificial intelligence (AI) and its associated risks in German quality media. The analysis identified six major categories, which accounted for the recurring themes and motifs used in these visual representations. In terms of sources of the visual elements, it can be said that some images come from established sources like Deutsche Presse Agentur or stock photo sites; others were created using generative AI. Below are the key findings from each main category.
The analysis of visual representations of artificial intelligence (AI) in German quality media has revealed several recurring categories and subcategories, each contributing to a broader understanding of how AI risks are visually framed. These categories reflect both stereotypical and evolving approaches to depicting AI and its implications for society. By identifying these categories, we can better understand the media’s role in shaping public perceptions of AI, often using simplified imagery that may obscure the technology’s complexity.
The following categories emerged from the data:
Depiction of Anthropomorphic Robots (RQ1-1, RQ1-2) emerged as a significant category, appearing in 13% of the total images and present in 39% of the analysed material. Subcategories included Anthropomorphic Robots in Interaction with Humans (RQ1-1) and Anthropomorphic Robots without Interaction with Humans (RQ1-2). These subcategories highlight the persistent portrayal of AI as humanoid robots, either interacting with humans or existing independently. This simplification of AI as a physical, human-like entity reflects a popular science fiction trope, where AI is depicted as embodied in mechanical figures. Although AI is primarily an abstract and non-physical technology, the media continue to reinforce the stereotype of AI as robots, reducing the complexity of the technology to something tangible and relatable. The relatively high frequency of these depictions points to a strong cultural association between AI and humanoid robots, which oversimplifies its potential societal roles.
Depiction of Humans and/or Human Body Parts (RQ1-3, RQ1-4, RQ1-5, RQ1-7, RQ1-11) was the most prevalent category, accounting for 35% of the total images and appearing in 78% of the analysed visual elements. This category included subcategories such as Human Brain Merged with Technical Elements (RQ1-3) and Human Head Merged with Technical Elements (RQ1-4), which visually merge human and machine elements. Other common subcategories included Depiction of Hands (RQ1-5) and Depiction of Eyes (RQ1-7), both of which suggest a focus on human interaction with technology. The subcategory Abstraction of Humans (RQ1-11) highlights more generalised representations of humans in abstract form. These images evoke AI’s direct impact on human life, highlighting the merging of human capabilities with technological enhancement. The dominance of these visuals in the dataset suggests that media coverage of AI is focussed primarily on the relationship between humans and AI, reflecting societal concerns about AI’s integration into everyday life and its potential to augment or replace human capabilities.
Blue/Grey Colour Scheme (RQ1-6) was another prominent category, representing 18% of the total images and appearing in 52% of the material studied. This aesthetic choice conveys neutrality, coldness, and futuristic detachment, reinforcing the idea of AI as a distant, clinical, and impersonal force. The widespread use of this colour palette reflects a broader cultural association between technology and these detached tones, further distancing AI from its human impacts. The prevalence of this colour scheme suggests that media frequently frame AI in a way that emphasises its technical and inhuman characteristics, contributing to public perceptions of AI as something beyond ordinary human control.
Depiction of Computers (RQ1-8, RQ1-10) was another significant category, appearing in 23% of the total images and 47% of the analysed material. The subcategories included Depiction of (Pseudo-)Computer Code (RQ1-8) and Depiction of Computers/Computer Components (RQ1-10). These images emphasise AI’s technical foundation and its association with computing systems, underscoring its computational origins. The frequent use of computer-related visuals suggests that media outlets focus heavily on the technical aspects of AI, reinforcing the perception of AI as a machine-driven entity rather than a social or creative tool.
An especially intriguing category was AI-Generated Images (RQ1-9), which accounted for 7% of the total images and appeared in 21% of the material. This subcategory, Image Created by AI (by the Article Author) (RQ1-9), highlights the growing irony in media production: articles about AI risks, including fears of job losses, were illustrated with AI-generated images. This not only exemplifies the immediate impact of AI on the creative industries but also underscores the irony that AI is already displacing professional illustrators, even in media discussing the dangers of AI to the job market. The prevalence of this category reflects a significant shift in media production processes, where AI is increasingly being used as both the subject and the tool of visual representation.
Finally, the category No Relevant Category / Other Depictions (RQ1-12) represented only 1% of the total images, appearing in 4% of the visual accompaniments. This small subset of images that did not fit into any established categories suggests that, while certain visual themes have become standardised in media representations of AI, there remains occasional variability, indicating a level of experimentation and adaptation as the discourse on AI continues to evolve.
In conclusion, these categories reflect the media’s reliance on familiar and sometimes clichéd visual motifs to depict AI. The anthropomorphisation of AI as robots, the merging of human and machine imagery, and the use of AI-generated images all reveal the complexities and contradictions inherent in how AI is framed in public discourse. The dominance of human-AI interaction depictions and the frequent use of technical imagery indicate that media representations are shaped by both cultural anxieties and the need for clear, engaging visuals.
Conclusion
Representing complex social and digital phenomena presents a significant challenge for journalists, particularly in an era where visual media often dominate communication (Shifman). This difficulty becomes especially pronounced in the depiction of technological innovations and their potential risks. While innovations are frequently hailed as engines of progress and economic growth, media portrayals tend to simplify and aestheticise these developments, often masking the intricate and ambivalent realities inherent to technological and societal change.
In contemporary visual culture, there is an increasing demand for imagery that not only informs but also evokes emotional responses and captures attention. Consequently, the media often resort to stereotypical and reductive representations in an effort to communicate the complexity of these phenomena. However, such portrayals risk distorting the perception of reality by reinforcing stereotypes and potentially overshadowing the positive dimensions of innovation. They also underline the abstract nature of the technology, hinting at components of the “blackbox” that was described by Luhmann in the 1980s and is currently the subject of much interdisciplinary research (Geitz et al.)
This study is based on a relatively small sample and a limited timeframe, covering articles published between January and September 2024. Due to the narrow scope of the research, the findings should not be seen as broadly generalisable or indicative of all media representations of AI. Rather, this analysis serves as an initial inventory of how AI risks are visually depicted in a specific context. As such, it highlights trends that require further exploration, and future research should expand on these results with a larger dataset and over an extended period to develop more comprehensive conclusions.
The analysis has shown that German media frequently rely on clichéd visuals, which ultimately reinforce a narrow and fear-inducing portrayal that fails to capture the full complexity of these phenomena. They include the Depiction of Anthropomorphic Robots, the Depiction of Humans and/or Human Body Parts, the use of a Blue/Grey Colour Scheme, and the Depiction of Computers, all of which are well-established visual codes for innovation and their potential risks, showing evidence of what Goode calls an “emergent entity that sits uneasily between a (Western) dichotomy of life and non-life, one that appears capable of harbouring an apocalyptic capacity to overturn human life as we know it“ (203).
Perhaps the most ironic and thought-provoking category is AI-Generated Images. In a striking twist, some articles discussing the dangers of AI, including fears of job losses, were illustrated with AI-generated images by the authors of the very same text—quite literally replacing the job of a professional illustrator. This irony adds another layer of meaning to the analysis, as the very technology being criticised for its potential to replace human labour is already doing so in the production of the articles themselves. It raises questions about the ethics and implications of AI in creative industries and demonstrates the immediate, real-world impact of AI on human employment.
The study has also highlighted the challenges of communicating intricate and rapidly evolving technological issues. The fragmented and fast-paced nature of contemporary media, along with the contextual nature of meanings attributed to technology, complicates the task of presenting a balanced and nuanced view (Sturdee et al.)
Overall, this research has underscored the need for more differentiated and realistic visual representations in media coverage of technological innovations. By moving beyond stereotypical imagery, the media could provide a more accurate portrayal of the complexities surrounding these advancements. While these visuals help communicate the abstract concept of AI to a broad audience, they may also reinforce stereotypes that oversimplify the technology and its societal implications. The study highlights the need for more nuanced and accurate portrayals of AI in the media, to foster a more informed and comprehensive public understanding of this rapidly evolving field.
Outlook
This research could be extended in several meaningful ways. One approach would be to expand the analysis to other countries, particularly those in the Anglo-American world, which are often described in research as more optimistic about technological advancements (Gerybadze). Additionally, a combined approach of textual analysis, examining the interaction between visual elements and the narrative content of written texts, could be promising for identifying potential discrepancies. Furthermore, conducting guided interviews with the editors responsible for visual content selection could provide valuable insights into the reasoning behind these choices, as well as a deeper understanding of the messages and meta-messages conveyed through the use of specific visual motifs.
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