Artificial intelligence (AI) in forensic entomology presents opportunities and challenges for method standardization and reproducibility. Forensic entomology has a rich history of use and continues to be a vital tool in estimating postmortem interval (PMI) and identifying corpse location. However, estimating PMI and species through entomology has a tendency toward subjective and vulnerable assumptions regarding the human error margin, taxonomic expertise, and environmental factors. In this chapter, we examine the emergent applications of AI computational tools in forensic entomology, reviewing machine learning, convolutional neural networks, and image classification algorithms to automatically identify insects and estimate PMI. We summarize the characteristics of gradient boosting and deep learning algorithms with the highest reported accuracy, evaluate AI studies assessing field performance and benchmark results against non-AI methods, assess how AI may improve reproducibility in research, analyze how AI captures the high dimensionality of ecological data, incorporate real-time analysis, and assess the reproducibility of AI results incorporating ecological factors. We also probe the computational and ethical limitations of AI forensic applications, such as dataset bias and representativeness, algorithm interpretability, and admissibility issues. Synthesizing findings from interdisciplinary studies, we conclude that there is an urgent need to standardize open-access entomological datasets and interpretable models within forensic entomology. This chapter outlines the proposed research agenda toward this goal and aims to guide computational researchers conducting unintended consequential research and forensic scientists employing computational methods.