Human-centric AI assessments refer to evaluations that prioritize human input and perspectives in AI decision-making processes. These assessments are crucial because they directly influence the fairness and reliability of AI systems across various sectors. In a typical human-centric AI assessment, data is collected from diverse sources and processed through AI systems. Human analysts then interpret this AI-generated data, which can introduce biases and misinterpretations. Consequently, the decisions implemented based on these assessments may lead to unintended and potentially harmful outcomes. Understanding the limitations of human-centric AI assessments can lead to improved decision-making accuracy, increased transparency, and enhanced trustworthiness of AI systems. For instance, acknowledging inherent biases can lead organizations to adopt more objective methods, thus fostering fairer outcomes. In practical applications, such as healthcare and financial risk assessment, recognizing these issues is vital for avoiding detrimental consequences, like unequal treatment outcomes or significant financial losses. However, the reliance on human interpretation in these assessments introduces significant challenges. For example, human biases can skew results, leading to unfair outcomes that may not reflect the true data. Additionally, in scenarios where data is incomplete, human-centric assessments might provide misleading conclusions that could adversely affect critical decisions.