# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
Due to their capacity to capture non-linear relationships and interactions, these models consistently outperform traditional linear models [29; 32; 33]. However, a significant challenge arises when these models are adopted in real-world scenarios due to their lack of interpretability [34]. As black-box models, they provide limited visibility into their internal decision-making processes, causing a trade-off between accuracy and interpretability [35; 36; 37; 38; 39; 40]. Recently, eXplainable Artificial Intelligence (XAI) techniques have been explored to enhance model transparency and interpretability, addressing the black-box nature of complex ML models. Integrating fairness, transparency, and accountability in AI systems is paramount to addressing and mitigating biases within data sources and algorithms. [PERSON] (2023) [41] highlighted that combating bias and discrimination involves encouraging fair and unbiased data-handling practices that ensure equal treatment for all individuals, regardless of protected characteristics such as race or gender. This approach is further supported by using synthetic data to address existing dataset biases, thus promoting better representation of populations and fostering more equitable outcomes. [PERSON] et al. (2023) [42] emphasized the critical role of Explainable AI in detecting flaws and biases within systems, thereby ensuring the transparency of these systems. XAI techniques not only aid in reducing biases in data but also facilitate a deeper understanding of the problem at hand, promoting ethical AI practices that are understandable and interpretable by humans. The necessity of integrating ethical considerations into AI development is underscored to prevent the perpetuation of societal biases and ensure equitable outcomes. As noted by [PERSON] et al. (2020) [PERSON] et al. (2020), the fairness discipline includes methods for bias detection within datasets, especially those concerning sensitive data that affect protected groups via variables like gender and race. Ethical concerns with black-box models arise due to their potential to unintentionally create unfair decisions by considering sensitive factors. Proposals centered on fairness aim to discover correlations between non-sensitive and sensitive variables, detect imbalanced outcomes that penalize specific subgroups, and mitigate the bias effect on model decisions. Furthermore, the Royal Society (2019) [43] notes that data collection issues can significantly impact the performance of machine learning systems, with image recognition systems failing to work accurately for minority ethnic groups. This challenge highlights the broader ethical challenges of AI, including fairness, transparency, and privacy protection, as emphasized by [PERSON] et al. (2021) [6]. Collectively, these insights stress the importance of addressing biases in data and algorithms to ensure fairness, transparency, and accountability in AI systems. Via the development and implementation of XAI techniques and other bias mitigation strategies, such as the use of synthetic data, it is possible to promote ethical AI practices that are not only understandable and interpretable by humans but also ensure that AI systems do not perpetuate existing societal biases, thereby ensuring equitable outcomes for all individuals. Like in many sectors, the importance of interpretability cannot be overstated, and in the real estate market, stakeholders, including investors, policymakers, and urban planners, often demand clear and comprehensible explanations behind predictions as they plan and make informed decisions [4]. Here, XAI techniques provide insights into the factors influencing selling prices and help stakeholders make informed decisions [27; 30; 32; 35; 44]. Developing XAI techniques conveys a promising solution for enhancing the potential use of AI in smart cities as it enables unboxing black-box AI models and explicitly describes their mechanisms, thus providing transparency, interpretability, and informed decision-making processes [45].