Current artificial intelligence models fundamentally fail to account for the complex trade-offs required for sustainable tourism because computer scientists and tourism managers define “fairness” in incompatible ways.
A new study by researchers at Graz University of Technology (TU Graz), the University of Graz and the Know Center reveals that algorithmic decision-support systems often unintentionally direct tourist streams in ways that negatively affect the environment and local communities.
While digital tools help tourists decide which attractions to explore, the computer science community currently lacks a sufficient understanding of the complex real-world relationships among stakeholders.
The research team, which conducted a semi-systematic review of 44 publications across both domains, found that computer science primarily addresses fairness through descriptive factors such as measurable discrimination.
“AI-supported recommender systems can have a major influence on purchasing decisions or the development of guest and visitor numbers,” says Bernhard Wieser from the Institute of Human-Centred Computing at TU Graz. “They provide information on services or places worth visiting and should ideally take individual needs into account. However, there is a risk that certain groups or aspects are under-represented.”
Critical disconnect
The analysis highlights a critical disconnect in how different industries approach problem-solving.
Tourism management actively utilises qualitative, inclusive and participatory methods to identify specific stakeholder needs, viewing fairness from a normative and holistic perspective.
In contrast, computer science relies heavily on a few mathematically formalised fairness criteria that fail to capture the multidimensional nature of fairness in tourism.
This technical limitation has real-world consequences.
Algorithms optimised for user satisfaction often struggle to ensure multistakeholder fairness, leading to issues such as overtourism, environmental pollution, unaffordable housing for residents and unfair distribution of economic benefits.
The researchers used a cycling tour app from Graz-based startup Cyclebee to investigate these dynamics, finding that “fairness” is a multi-stakeholder problem involving service providers, municipalities and even local residents who do not use the app but feel the effects of tourism traffic.
Competing interests
The study argues that reconciling these competing interests cannot be solved by technology alone.
“If the app is to deliver the fairest possible results for everyone, the fairness goals must be clearly defined in advance,” says Wieser. “And that is a very human process that starts with deciding which target group to serve.”
To bridge the gap, the researchers propose using participatory design methods to involve all actors in the development process.
However, the team acknowledges that mathematical optimisation has limits.
“Ultimately, however, you have to decide in favour of something, so it’s up to the individual,” says Dominik Kowald from the Fair AI group at the Know Center research centre and the Institute of Digital Humanities at the University of Graz. “Not everything can be optimised at the same time with an AI model. There is always a trade-off.”
A necessary step
The findings suggest that future interdisciplinary collaboration is a necessary step to enhance algorithmic decision-support systems.
By translating the abstract fairness principles of tourism management into computable targets, systems could align with broader goals of sustainability and equity rather than just predictive accuracy.
“Our study results are intended to support software developers in their work in the form of design guidelines, and we also want to provide guidelines for political decision-makers,” says Wieser. “It is important that we make recommender systems increasingly available to smaller, regional players thanks to technological developments. This would make it possible to develop fair solutions and thus create counter-models to multinational corporations, which would sustainably strengthen regional value creation.”
