Ethical Concerns in Leveraging AI for Phytochemistry

Artificial intelligence (AI) has become a paradigm that has changed the manner in which plant-derived products are detected, characterized, and utilized in botanical sciences, phytochemistry, and pharmaceutical research. This chapter summarizes the whole subject of AI-based innovations including machine learning and deep learning along with advanced computational chemistry and multi-omics integration, which are speeding the discovery of phytochemicals and enhancing agricultural and drug discovery processes. The integration of AI with quantum mechanical methods including the Density Functional Theory (DFT) and molecular dynamics has significantly improved the accuracy of molecular modeling and structural clarification and prediction of bioactive metabolic pathways. Moreover, the Internet of Things (IoT) applications powered by AI have transformed digital agriculture enabling it to make data-driven decisions, optimize resources, and enhance crop productivity. Even though these developments have been made, significant issues such as the bias in data, lack of consistency in datasets, lack of model interpretability, potential ethical issues, data privacy, and workforce training still pose significant challenges to the full-scale implementation of AI in phytochemical and botanical studies. New ethical principles of the worldwide organizations and the necessity of transparency, accountability, and interdisciplinary cooperation are also mentioned in this chapter. In the future, AI-driven phytochemistry can be improved with the creation of quality worldwide databases, explicable AI frameworks, and hybrid frameworks of computation that incorporate plant biology, quantum mechanics, and machine learning. Through responsible innovation and scientific rigor, AI can unlock the huge chemical library of plants, speed up the discovery of natural products-based drugs, and enable sustainable development in the decades to come.