## 2 Materials and Methods
### Dimensions of Data Challenges for AI
#### 2.2.4 Dimension IV: Bias and Fairness
Sampling Bias: Sampling bias occurs when the collected data are not representative of the population of interest. This can lead to biased AI models, as they learn from a non-representative sample [132]. Aggregation Bias: Aggregation bias emerges when data are combined from multiple sources with different characteristics or distributions. This can cause AI models to learn patterns that are not generalizable to the entire population [133]. Confirmation Bias: Confirmation bias emerges when data or information are selectively chosen or weighted to support pre-existing beliefs or expectations. This can inadvertently affect AI model outcomes because the training data may disproportionately represent certain aspects or patterns [134]. Group attribution bias: This bias arises when AI systems generalize, or stereotype individual behaviors based on the perceived characteristics of the group to which they belong. This can lead to biased predictions that do not accurately represent an individual's unique attributes [135]. Temporal Bias: Temporal bias occurs when AI models are trained on historical data that no longer reflects current trends or patterns. This can lead to biased predictions because the models fail to adapt to changes in the underlying data distribution over time [136]. Feature Selection Bias: Feature selection bias emerges when certain features are given greater importance or focus during the model development process, leading to biased  
Figure 12: Types of data bias. outcomes. This can be a result of domain-specific biases or biases inherent to the algorithms used for feature selection [137]. Anchoring Bias: Anchoring bias occurs when AI models rely heavily on initial information or data points to make predictions. This can result in biased outcomes, as the models may not sufficiently consider other relevant factors or adjust their predictions based on new information [138]. Automation Bias: Automation bias refers to the tendency of humans to over-rely on AI system outputs, even when they are flawed or biased. This can exacerbate the consequences of biased AI models because users may not question or scrutinize biased decisions or recommendations generated by these systems [139]. Consequences of Data Bias and Unfairness in AI  
Discrimination: Biased AI systems can inadvertently discriminate against certain groups or individuals, thereby leading to unfair treatment. For example, biased facial recognition systems can misidentify individuals from minority groups at a higher rate than those from majority groups [140]. Misinformation: AI systems trained on biased data may propagate false or misleading information, exacerbating existing stereotypes and prejudices [141]. Legal and Ethical Implications: Biased AI systems can pose legal and ethical challenges as they may violate anti-discrimination laws or ethical guidelines [142]. Addressing Data Bias and Fairness in AI  
This section proposes a solution to address data bias and fairness in AI. These results are shown in Figure 13.  
Figure 13: Proposed solutions to the data bias and fairness challenge. Data Collection and Pre-processing: Collecting diverse and representative data is crucial for mitigating data bias [143]. Additionally, preprocessing techniques such as resampling, reweighting, or data augmentation can help reduce bias in the dataset [144]. Algorithmic Fairness: Researchers have proposed various fairness-aware machine-learning algorithms that aim to minimize discriminatory outcomes ([PERSON] et al., 2019). These techniques typically incorporate fairness constraints into the model training process or post-process model predictions to ensure fairness [145]. Fairness Metrics: Developing appropriate fairness metrics is essential for quantifying and comparing the performance of AI models in terms of fairness [146]. Some commonly used metrics include demographic parity, equalized odds, and disparate impact ratios [147].