## 2 Materials and Methods
### Dimensions of Data Challenges for AI
#### 2.2.4 Dimension IV: Bias and Fairness  
AI has seen rapid advancements over the past decade, transforming many aspects of our lives. However, as AI systems have become more prevalent, concerns regarding data bias and fairness have emerged. The performance of AI models depends heavily on the quality of the data used for training, and biased data can lead to biased outcomes [124]. This section provides a comprehensive review of the challenges related to data bias and fairness in AI, with a focus on recent research and solutions. Bias, in the context of artificial intelligence and machine learning, refers to systematic errors in the algorithms' predictions or decisions resulting from skewed training data, flawed algorithms, or the influence of pre-existing assumptions. These biases can lead to unfair or discriminatory outcomes, impacting individuals or groups based on attributes such as race, gender, age, or socioeconomic status. Addressing and mitigating bias in AI systems is crucial for ensuring the fair and ethical deployment of these technologies in various domains, from healthcare and finance to criminal justice and social media. Amazon's AI Recruiting Tool: In 2018, Amazon discontinued its AI recruiting tool after it was found to be biased against female candidates. The system was designed to review resumes and rank candidates based on their qualifications. However, the model was trained on resumes submitted to the company over a ten-year period, which predominantly belonged to male candidates. Consequently, the AI system preferred male candidates over equally qualified female candidates [125]. Google Photos' Racial Bias: In 2015, Google Photos was criticized for its image recognition algorithm, which mistakenly labeled African Americans as gorillas. This incident highlighted racial bias in the AI system, which was attributed to the lack of diversity in the training data. Google apologized for this mistake and worked on improving the algorithm to avoid such issues in the future [126]. Microsoft's Tay Chatbot: In 2016, Microsoft launched a Twitter-based AI chatbot called Tay. The chatbot was designed to learn from user interactions and to mimic human conversations. However, within 24 h of its launch, Tay started posting offensive and racist tweets, as it had learned from malicious users who intentionally fed biased and inappropriate content. Microsoft quickly took Tay offline and apologized for the incident [127]. Apple Card's Gender Bias: In 2019, Apple faced a backlash when its Apple Card, a credit card service powered by Goldman Sachs, was accused of gender bias. Several users reported that the credit limit offered to male applicants was significantly higher than that offered to their female counterparts despite having similar or even worse financial profiles. This issue raised concerns about the fairness and transparency of AI algorithms used for credit assessment [128]. COMPAS Risk Assessment Tool: The Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) tool is an AI-based system used in the United States to assess the risk of recidivism in criminal defendants. A 2016 investigation by ProPublica revealed that the tool exhibited racial bias, with African American defendants being more likely to be incorrectly labeled as high-risk compared to white defendants with similar criminal records. This controversy led to increased scrutiny of AI-based risk assessment tools in the criminal justice system [129]. Figure 12 represents ten types of data bias. Measurement Bias: Measurement bias occurs when data collection methods systematically over- or under-represent certain features or aspects of data [130]. This can lead to AI models that generate biased predictions that are not representative of the true population. Label Bias: Label bias arises when labels assigned to data instances are incorrect or unrepresentative of true outcomes. This can result from human errors, subjective judgments, or systemic issues during the labeling process [131].