Investigation and Analysis of Bias Reduction Methods and Equity Enhancement in Data Mining Algorithms

Data mining has become a cornerstone for extracting valuable insights and making data-driven decisions across a variety of fields. However, biases present in both the data and the algorithms can lead to unfair, inaccurate, and inequitable outcomes, particularly in sensitive areas such as recruitment, credit evaluation, and legal decision-making. Despite the growing body of research addressing algorithmic bias, challenges persist in developing effective methods to reduce bias while maintaining accuracy. This review aims to provide a comprehensive overview of current approaches to mitigating bias and enhancing fairness in data mining algorithms. We explore a wide range of techniques, including data preprocessing methods, fairness-aware algorithms, and evaluation metrics for assessing fairness. The review also discusses key challenges such as balancing fairness with accuracy, ensuring interpretability, and addressing ethical concerns. By synthesizing existing research, this paper identifies gaps in the current literature and highlights promising directions for future research. The findings underscore the importance of addressing algorithmic bias in ensuring ethical, transparent, and equitable decision-making in data-driven systems.

کلیدواژه ها:

Data mining algorithms ، data analysis ، data biases ، unjust outcomes