Foresight Bias in Decision Accuracy and Prevention Frameworks: A Systematic Literature Review

Decision-making accuracy is often compromised by cognitive biases, particularly foresight bias, which distorts future-oriented judgments and leads to suboptimal outcomes in both human and AI-based systems. This research investigates the role of foresight bias in influencing decision accuracy and examines various prevention frameworks to mitigate bias in decision-making processes through a Systematic Literature Review (SLR) approach. A total of 20 recent peer-reviewed articles were systematically analyzed following PRISMA guidelines to identify patterns related to cognitive bias, predictive decision-making, and bias mitigation strategies. The findings reveal that foresight bias significantly reduces decision accuracy by distorting future-oriented judgments, particularly in complex and uncertain environments, and is further reinforced by cognitive tendencies such as overconfidence and illusion of control, as well as biases embedded in artificial intelligence (AI) and machine learning systems. Moreover, the interaction between human and algorithmic bias increases the likelihood of suboptimal decisions, especially in forecasting and data-driven contexts. The research also highlights that the negative impact of bias can be effectively minimized through the implementation of prevention frameworks, including debiasing strategies, explainable AI, human-in-the-loop approaches, and multi-objective optimization, which collectively enhance transparency, accountability, and decision quality. This research contributes to the literature by providing a comprehensive synthesis of foresight bias and decision accuracy, while offering practical insights for improving decision-making in complex and digitalized environments.