The Impact of Bias in Machine Learning Models

Abstract

Machine learning (ML) has significantly transformed multiple domains by facilitating data-driven decision-making and automation. However, as these algorithms gain traction in various sectors—such as finance, criminal justice, and healthcare—concerns surrounding issues of bias, transparency, and scalability have intensified. This proliferation raises critical ethical, moral, and fairness considerations, alongside questions of accountability. This study provides a comprehensive overview of these pressing challenges and offers a critical appraisal of existing proposed solutions to mitigate them. Specifically, it examines strategies for improving model transparency and interpretability, explores methods for scaling ML systems to accommodate vast datasets and real-time applications, and assesses the potential for biases in training data to lead to skewed outcomes. Furthermore, this research delves into the ethical dilemmas posed by deploying ML models in sensitive fields, presenting viable solutions for these challenges. By aggregating current literature, this work furnishes insightful analyses to promote the ethical and responsible application of machine learning technologies across various sectors.