🤖 AI Summary
This study systematically investigates fairness in computer vision (CV) and natural language processing (NLP) models operating on unstructured data, with a focus on how algorithmic bias exacerbates systemic inequities. Method: Leveraging real-world Kaggle datasets, we construct end-to-end ML pipelines and conduct the first empirical comparison of two leading fairness toolkits—Fairlearn (Microsoft) and AI Fairness 360 (IBM)—across CV and NLP tasks, evaluating their metric coverage and bias mitigation efficacy. Contribution/Results: Fairlearn excels in interpretability and engineering integration, whereas AIF360 offers broader multidimensional fairness metrics. Combining preprocessing and postprocessing techniques reduces bias by 32–47% on average. We further propose a three-tier industrial fairness governance framework, providing both methodological guidance and empirical benchmarks for cross-modal AI fairness assessment and deployment.
📝 Abstract
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.