Machine learning (ML) algorithms play a crucial role in decision making across diverse fields such as healthcare, finance, education, and law enforcement. Despite their widespread adoption, these systems raise ethical and social concerns due to potential biases and fairness issues. This study focuses on evaluating and improving the fairness of Computer Vision and Natural Language Processing (NLP) models applied to unstructured datasets, emphasizing how biased predictions can reinforce existing systemic inequalities. A publicly available dataset from Kaggle was utilized to simulate a practical scenario for examining fairness in ML workflows. To address and mitigate biases, the study employed two leading fairness libraries: Fairlearn by Microsoft, and AIF360 by IBM. These tools offer comprehensive frameworks for fairness analysis, including metrics evaluation, result visualization, and bias mitigation techniques. The research aims to measure bias levels in ML models, compare the effectiveness of these fairness libraries, and provide actionable recommendations for practitioners. The results demonstrate that each library possesses distinct strengths and limitations in evaluating and mitigating fairness. By systematically analyzing these tools, the study contributes valuable insights to the growing field of ML fairness, offering practical guidance for integrating fairness solutions into real world applications. This research underscores the importance of building more equitable and responsible machine learning systems.

翻译：机器学习（ML）算法在医疗、金融、教育和执法等不同领域的决策过程中发挥着关键作用。尽管这些系统已被广泛采用，但由于潜在的偏见和公平性问题，它们引发了伦理与社会担忧。本研究重点评估和改进应用于非结构化数据集的计算机视觉与自然语言处理（NLP）模型的公平性，强调有偏预测如何加剧现有的系统性不平等。研究利用Kaggle的公开数据集模拟实际场景，以检验机器学习工作流程中的公平性问题。为应对和缓解偏见，本研究采用了两个主流的公平性工具库：微软的Fairlearn和IBM的AIF360。这些工具提供了完整的公平性分析框架，包括指标评估、结果可视化和偏见缓解技术。本研究旨在测量机器学习模型中的偏见程度，比较这些公平性工具库的有效性，并为从业者提供可操作的改进建议。结果表明，各工具库在评估和缓解公平性问题方面均具有独特的优势与局限。通过对这些工具进行系统分析，本研究为快速发展的机器学习公平性领域提供了重要见解，并为在实际应用中集成公平性解决方案提供了实用指导。此项研究强调了构建更公平、更负责任的机器学习系统的重要性。