Addressing AI Systemic Bias and Injustice: Obstacles and Solutions

Abstract

The increasing prevalence of AI in decision-making systems has raised valid concerns over equity and bias, especially in sectors with high stakes such as healthcare, banking, employment, and criminal justice. Machine learning models are typically touted as being objective, but they can actually inherit and amplify biases in the training data, algorithms, or system architecture. These prejudices have the potential to have a disproportionate effect on some demographics, which could cause people to lose faith in AI systems and even cause discrimination. the root causes and consequences of AI bias, which encompass prejudice in data, bias in algorithms, and bias in humans. Some of the subjects explored include imbalanced datasets, historical inequalities, and problems with feature selection. Biased predictions and conclusions may result from these errors. In order to evaluate and quantify bias in ML models, the research digs further into crucial equity metrics like demographic parity, equal opportunity, and disproportionate effect. In light of these concerns, the paper investigates a range of mitigating strategies that aim to advance AI justice. Data rebalancing and bias correction are examples of pre-processing approaches that incorporate fairness requirements into model training. Post-processing processes change model outputs to ensure equitable outcomes. Accountability and justice can be enhanced through the use of openness, explainable artificial intelligence (XAI), and regulatory frameworks, which are also discussed.