Impact Assessment

Impact assessment analyzes the effects of interventions or events, aiming to understand and quantify their consequences. Current research focuses on developing robust methods for evaluating impacts across diverse domains, including AI systems, climate change, and public health, often employing machine learning models like deep learning networks, graph neural networks, and causal inference techniques to analyze complex datasets and account for biases. These advancements are crucial for informing evidence-based decision-making in various sectors, improving the design of interventions, and mitigating potential negative consequences. The field's significance lies in its ability to provide a quantitative understanding of cause-and-effect relationships, leading to more effective policies and strategies.