Experiential Consequence

Experiential consequence research explores how actions and events lead to downstream effects, encompassing diverse fields from legal ramifications of misinformation to the impact of AI bias in healthcare. Current research focuses on predicting consequences using causal graphs and advanced machine learning models, including graph neural networks and generative models, to analyze complex interactions and improve decision-making. This work is crucial for mitigating societal harms stemming from misinformation, improving the fairness and reliability of AI systems, and understanding the broader implications of human-computer interaction and technological advancements.