Artificial intelligence is transforming communities, but those who study these systems and those who use them have very different expectations about the future. A recent survey revealed that technology experts generally have a more optimistic vision of artificial intelligence than the general public and prioritize its potential benefits over the associated risks. The research results were published in the journal AI & SOCIETY.
Lead author Philipp Brauner, a communications researcher at RWTH Aachen University in Germany, and his colleagues wanted to understand how different groups envisioned the trajectory of automation. The researchers focused on mapping the mental models of different populations. A mental model is an individual’s internal understanding of how something works, what capabilities it has, and what consequences it has in reality. By comparing these internal frameworks, the team hoped to uncover potential friction points in technology implementation.
Certain risks arise when engineers design tools based primarily on their own optimistic mental models. They may be unconsciously building what researchers call “raw artificial intelligence.” The term comes from the Greek myth of Procrustes, an unscrupulous blacksmith who forced iron beds on his customers by elongating and amputating their limbs. In the context of modern technology, we describe systems that force ordinary people to adapt to rigid technological constraints, rather than modifying the actual technology to suit diverse human needs.
To map these underlying views, the research team recruited 1,110 German nationals along with 119 academic artificial intelligence experts. This public group represented different aspects of society in terms of age and socio-economic status. The expert group is comprised primarily of German and Dutch researchers and actively informs policy, educates practitioners, and builds algorithms.
Each participant completed an online survey evaluating randomly selected statements from a master list of 71 hypothetical scenarios. To create this comprehensive list, researchers utilized a sociological framework that divides society into core subsystems. These subsystems include the economy, legal system, science, politics, religion, and education. Ensuring that all sectors were represented, the final scenarios covered a wide range of potential applications and impacts expected over the next decade.
Topics ranged from everyday automation, such as driving a car or teaching students, to more speculative outcomes. Some participants evaluated the potential of software to behave according to moral concepts or solve social problems. Other scenarios presented dystopian possibilities, such as machines destroying humanity, increasing individual loneliness, and making independent decisions about human life and death.
Participants rated each assigned scenario across four different dimensions. They estimated the likelihood of this event occurring within 10 years. We also assessed the personal and social risks involved, potential benefits, and overall positive or negative feelings about the outcome.
The results highlight systematic differences in how experts and laypeople assess technological progress. On average, experts rated 71 scenarios as more likely to occur than the general public. At the same time, technical experts perceived lower personal risks and expected greater benefits overall. This means that there was an overall more positive evaluation of artificial intelligence among researchers compared to the general public.
Experts also offered much more diverse opinions, depending on the specific scenario at hand. Expert ratings vary widely, from very positive to very negative, suggesting that their views on this technology are highly differentiated. In contrast, the general public rated almost everything as close to the baseline of general concern.
Not only did the two groups score things differently, but they also used different internal formulas to weigh the good against the bad. Both groups exhibited what psychologists call an affective heuristic. This is a mental shortcut in which individuals view something that is highly beneficial as inherently low risk, and something that is highly risky as less beneficial. Due to this psychological mechanism, risk and benefit scores were inversely related in both groups.
When determining the overall value of a particular scenario, experts were heavily influenced by perceived benefits. In the expert group, the expected benefits made the final decision about three times stronger than the perceived risks. High risk expectations did little to move experts’ overall enthusiasm for the tool, provided the associated benefits were also high.
To the average person, this calculation looked completely different. Everyday citizens also valued practicality and convenience, but were far more sensitive to potential downsides. In the public sample, perceived risk had a much greater impact on the overall value of the scenario. When everyday users perceived a danger in the application of technology, the threat greatly dampened their public approval.
This difference in risk calculations has led to significant disagreements on certain topics. The people expressed high expectations and deep concern about the existential threat. They feared applications that could replace human relationships, control information, or lead to a complete loss of human control. On the contrary, experts considered these apocalyptic outcomes to be very unlikely and gave them low ratings.
Instead, academic groups expressed radical optimism about structural and scientific change. Experts assigned high probability and high value to scenarios in which artificial intelligence improves healthcare, increases environmental sustainability, and supports medical decision-making. They considered these positive results to be likely and very useful.
Despite these contrasting visions, the two groups agreed on some specific issues. Both samples recognized the significant risk that criminals could misuse this technology. They also shared positive views about artificial intelligence making medical diagnoses. The researchers suggest that identifying such narrow consensuses could help policymakers focus regulatory attention where it is most needed.
In an exploratory analysis, the researchers grouped the 71 scenarios based on participant ratings to look for broader trends. They found two major clusters of technology applications. The first cluster included support systems such as health monitors and smart city planners, which both groups largely approved. The second cluster was characterized by autonomous and dominant systems capable of making independent political decisions and deciding on wars. Although both groups were aware of these clusters, the public was much quicker to assign autonomous scenarios to the overarching category of significant threats.
As with any hypothetical future-based research, this study has limitations. Participants rated the imaginary scenarios in passing. This means that the results capture immediate emotional reactions rather than rational predictions. These affective heuristics still strongly dictate how people adopt and trust new tools, but they do not reflect guaranteed outcomes. Evaluating highly abstract vignettes also removes the nuances of how the tool is actually constructed.
Additionally, this study primarily sampled participants from Germany in early 2023. The data collection took place shortly after the generative language model sparked a significant surge in global media coverage. With such intense news cycles, public sentiment often changes rapidly. This means that concerns about these particular technologies may have evolved since the data was collected.
Future research should track these opinions over time and across different cultures. Expanding participants to include non-Western countries could reveal whether these risk profiles represent universal human anxieties or specific local attitudes. Current researchers also advocate incorporating qualitative interviews to better understand why everyday users group certain threats together.
Ultimately, this study points to the pressing need for participatory design practices in software development. If future algorithms are built solely according to the optimistic visions of technical experts, they may inadvertently ignore the legitimate safety concerns of the people who actually need to use them. Bridging this perception gap could help modern societies integrate automation in a way that truly respects human priorities.
The study, “Graphing the AI Perception Gap: Divergent views on risks, benefits, and value between experts and the public that challenge the social acceptance of AI,” was authored by Philipp Brauner, Felix Glawe, Gian Luca Liehner, Luisa Vervier, and Martina Ziefle.
