## 2 Materials and Methods
### Dimensions of Data Challenges for AI
#### 2.2.1 Dimension I: Data Quality  
Data quality is a critical aspect of AI. The accuracy, completeness, and consistency of the data used for training and testing AI models directly affects the performance and effectiveness of the AI system. Low-quality data can lead to biased, inaccurate, or irrelevant results, negatively affecting decision-making processes based on AI outputs. Therefore, ensuring the high quality of data is crucial for AI systems to produce reliable and valuable results. This may include data cleansing, validation, enrichment, and management. AI applications require high-quality relevant, representative, and reliable data to produce optimal outcomes. AI systems also require ongoing monitoring and maintenance to ensure that data quality is consistent over time. The performance of AI systems is heavily reliant on the quality of the data used for training and validation [37]. Data quality is a multidimensional concept that encompasses factors such as accuracy, completeness, consistency, and timeliness [38]. Ensuring data quality is a challenging task given the vast amount of data generated daily and the inherent complexity of data structures [39]. Figure 5 shows the challenging elements of data quality. Challenging Measures of Data Quality and Implications on AI Systems  
This section presents different measures to ensure the data quality for AI applications. Figure 6 represents the challenging measures of Data Quality. Figure 4: Dimensions of Data Challenges for AI. Figure 5: Challenging elements of data quality. Accuracy: Data accuracy is critical for AI to function effectively. Accuracy refers to the degree to which data are correct and error-free. In other words, it aims to describe the degree to which data correctly represent real-world phenomena [40]. AI systems require accurate data for training and validation to ensure accurate predictions and decisions [41]. Inaccurate data can lead to biased or erroneous outcomes, undermining the reliability and usefulness of AI systems [42]. Completeness: Completeness refers to the extent to which all relevant and sufficient data coverage is present in the dataset to provide insight and meaningful results for AI [37]. Incomplete data can lead to biased or unrepresentative AI models because the algorithms may not have sufficient information to learn the underlying patterns and relationships [43]. Missing data can be attributed to various factors such as data collection or data entry errors [44]. Consistency: Consistency refers to the uniformity of data representations and formats across a dataset [38]. In other words, this is the extent to which the data are free from conflicts, inaccuracies, or discrepancies when compared to other sources or systems. Inconsistent data can lead to confusion and misinterpretation by AI algorithms, resulting in suboptimal performance [45]. Ensuring consistency requires the standardization and harmonization of data formats, units, and terminologies [46]. Timeliness: Timeliness refers to the degree to which data are updated and relevant to the current context [37]. AI systems require timely data to adapt to dynamic environments and provide accurate predictions [47]. Outdated data may lead to poor performance and even harmful consequences because AI systems may not account for recent changes in the underlying phenomena [48]. Timeliness is particularly important in domains such as finance, healthcare, and transportation, where real-time insights offer significant advantages. For instance, if the data used to build a weather forecasting model are outdated, the model might not be able to make accurate predictions. Similarly, if the data used for training a stock market predictor are not timely, the model can make decisions based on outdated information that may not be relevant to the current state of the market. Therefore, data timeliness is an important dimension of AI data quality.