<p>Fairness is a critical concern in the integration of machine learning and AI—e.g., Generative AI, LLMs, and Agents—into decision-making, yet the adoption of fairness toolkits by software practitioners remains limited. This gap hinders efforts to operationalize fairness, especially when cultural and ethical values are overlooked. Given that fairness is socially constructed, individual cultural values may significantly influence how practitioners perceive and adopt fairness tools. The objective of this study is, therefore, to investigate whether and how individual cultural values influence software practitioners’ intention to adopt and actual use of fairness toolkits. Specifically, we integrate the UTAUT2 model with Hofstede’s cultural dimensions to examine both direct and moderating cultural effects within the adoption process. A survey of 181 software professionals was conducted, and data were analyzed using Partial Least Squares Structural Equation Modeling. Findings show that cultural values—specifically Power Distance, Collectivism, and Long-Term Orientation—not only directly affect adoption intention and behavior but also moderate key relationships in the adoption process. For example, collectivist individuals were more likely to act on their intention to use fairness tools, highlighting the importance of shared team goals. Overall, our findings indicate that fairness toolkit adoption is shaped not only by technology-related perceptions but also by culturally grounded value orientations. These results provide actionable insights for promoting fairness tool adoption through culturally aware strategies in software development environments.</p>