In the realm of forecasting, judgmental biases often hinder efficiency and accuracy. Algorithms present a promising avenue for decision makers to enhance their forecasting performance. In this overview, we scrutinized the occurrence of the most relevant judgmental biases affecting forecasting across 162 papers, drawing from four recent reviews and papers published in forecasting journals, specifically focusing on the use of algorithms. Thirty-three of the 162 papers (20.4%) at least briefly mentioned one of twelve judgmental biases affecting forecasting. Our comprehensive analysis suggests that algorithms can potentially mitigate the adverse impacts of biases inherent in human judgment related to forecasting. Furthermore, these algorithms can leverage biases as an advantage, enhancing forecast accuracy. Intriguing revelations have surfaced, focusing mainly on four biases. By providing timely, relevant, well-performing, and consistent algorithmic advice, people can be effectively influenced to improve their forecasts, considering anchoring, availability, inconsistency, and confirmation bias. The findings highlight the gaps in the current research landscape and provide recommendations for practitioners. They also lay the groundwork for future studies on utilizing algorithms (e.g., large language models) and overcoming judgmental biases to improve forecasting performance.