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Exploring Bias in the FOLD-R++ Algorithm: A Comprehensive Analysis

Raaghav Ramamoorthy*

As machine learning is being used in numerous applications, there is even more concern regarding algorithms within artificial intelligence. This paper focuses on analyzing the biases incorporated in a setup like FOLD-R++. The findings hold significance to both academia and industry in establishing what is a fair and neutral machine learning algorithm. The article contains an exhaustive literature review about biases in machine learning and a detailed explication.

The chapter re-examines previous studies on the efficacy of this algorithm, unearthing the limitations in earlier literature and urging more research. This involves purposefully choosing a dataset from Kaggle, metrics applied in evaluating the algorithm, and a detailed experimental design. The results of these tests of different test scenarios have displayed that the algorithm is correct but vulnerable to bias and efficient across different domains. The results are discussed, and comparisons with what is available in the table regarding system-biased algorithms are made. In conclusion, this study contributes to the existing literature on machine learning and highlights certain shortcomings concerning the use of the FOLD-R++ algorithm. Thus, it demonstrates the need to address issues of unexplained inequality and develop reliable algorithms for decision-making. The study acknowledges these shortcomings to the extent that it becomes a vital stage toward developing more justified machine learning.