Multistakeholder Fairness in Tourism: What can Algorithms learn from Tourism Management?

2025-08-28

AI Summary

Tourism algorithmic systems—such as recommendation engines—frequently generate negative externalities by neglecting real-world trade-offs among diverse stakeholders, including local communities, ecosystems, and cultural heritage. This stems from computer science’s insufficient conceptualization of complex sociotechnical contexts. Method: Drawing on a semi-systematic literature review, we integrate perspectives from tourism management and computer science to identify multistakeholder requirements and critically assess prevailing fairness practices in algorithmic design. Contribution/Results: We expose the limitations of purely mathematical fairness metrics and argue that interdisciplinary collaboration is essential for developing genuinely equitable tourism algorithms. The study proposes a feasible, domain-informed framework for algorithm design that incorporates normative, holistic conceptions of fairness from tourism management—emphasizing sustainability, community agency, and systemic equity—thereby advancing responsible innovation in tourism technology.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Search and Optimization: Algorithm Configuration

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Algorithmic decision-support systems, i.e., recommender systems, are popular digital tools that help tourists decide which places and attractions to explore. However, algorithms often unintentionally direct tourist streams in a way that negatively affects the environment, local communities, or other stakeholders. This issue can be partly attributed to the computer science community's limited understanding of the complex relationships and trade-offs among stakeholders in the real world. In this work, we draw on the practical findings and methods from tourism management to inform research on multistakeholder fairness in algorithmic decision-support. Leveraging a semi-systematic literature review, we synthesize literature from tourism management as well as literature from computer science. Our findings suggest that tourism management actively tries to identify the specific needs of stakeholders and utilizes qualitative, inclusive and participatory methods to study fairness from a normative and holistic research perspective. In contrast, computer science lacks sufficient understanding of the stakeholder needs and primarily considers fairness through descriptive factors, such as measureable discrimination, while heavily relying on few mathematically formalized fairness criteria that fail to capture the multidimensional nature of fairness in tourism. With the results of this work, we aim to illustrate the shortcomings of purely algorithmic research and stress the potential and particular need for future interdisciplinary collaboration. We believe such a collaboration is a fundamental and necessary step to enhance algorithmic decision-support systems towards understanding and supporting true multistakeholder fairness in tourism.

Problem

• Addressing algorithmic bias in tourism recommender systems
• Bridging tourism management and computer science for fairness
• Capturing multidimensional stakeholder fairness beyond mathematical criteria

Innovation

• Leveraging tourism management qualitative methods
• Synthesizing interdisciplinary literature review
• Addressing multidimensional fairness algorithm limitations