# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
## 2 Materials and Methods
### Methods
#### 2.2.4 Explainable Artificial Intelligence  
Machine learning systems are increasingly being adopted, pushing society toward a new era of algorithmic decision making [85]. While these systems become more integral to various domains, their decisions bear a higher potential for societal impact, emphasizing the importance of trust, transparency, and understanding these complex models. Many advanced ML models operate as black boxes. Their opaqueness poses challenges as users, regardless of expertise, cannot fully understand or verify the rationale behind the system's decisions [86]. Historically, the focus of AI has shifted toward predictive power, leaving interpretability behind. Given the complexities of black box models, there is a growing emphasis and need for machine learning interpretability to make these systems more transparent and trustworthy. Explainable Artificial Intelligence (XAI), a term coined by [PERSON] et al. [87], is an emergent field aiming to enhance the transparency of AI systems without compromising their predictive performance. XAI uses explanations to elucidate the predictions of ML models to users, serving as tools to achieve interpretability, typically relating the feature values of data to predictions in a comprehensible manner. XAI encompasses a wide range of model-specific and model-agnostic techniques to improve the interpretability of machine learning models. Model-specific methods are designed to work with specific types of ML models. They leverage the inherent characteristics and structures of those models to provide explanations. For example, the parameters within a linear regression model or the architecture of a decision tree inherently provide interpretability. For deep neural networks, techniques like feature visualization or class activation mapping can provide insights specific to the architecture. Model-agnostic methods offer explanations for interpreting black box models independent of the ML model type, regardless of its internal architecture. They operate post hoc, meaning that after the model has made a prediction, they do not interfere with its internal workings or performance. Examples include methods that provide model-agnostic explanations, such as LIME (Local Interpretable Model-agnostic Explanations) [88], which approximates predictions with local and simpler interpretable models, and SHAP [47], which calculates the contribution of each feature to the prediction by using Shapley values, a concept initially developed in cooperative game theory [48]. The SHAP method has been widely applied across various domains for interpreting machine learning models, including finance [89], healthcare [90], and environmental sciences [91], to provide interpretable explanations of complex machine learning models. In our study, we leverage the predictive capabilities of an XGBoost ML model and enhance its interpretability using SHAP. We aim to decode the model's predictions, evaluate each feature's significance, and provide a comprehensive view of the model's behavior and decision-making process. Using SHAP, we understand feature importance and identify the most influential features in making the predictions [47]. The SHAP method computes a value to each feature, representing its contribution to the model's prediction for a specific instance while considering all possible combinations of features. By attributing these Shapley-based values to each feature, the SHAP method enables an understanding of the relative importance of different features in influencing the model's predictions. The formula for computing SHAP values is based on Shapley values. The Shapley value formula, represented by Equation (8), calculates the contribution of each feature (or \"player\" in game theory) to the predictive model [47].