Prediction models can improve efficiency by automating decisions such as the approval of loan applications. However, they may inherit bias against protected groups from the data they are trained on. This paper adds counterfactual (simulated) ethnic bias to real data on mortgage application decisions, and shows that this bias is replicated by a machine learning model (XGBoost) even when ethnicity is not used as a predictive variable. Next, several other de-biasing methods are compared: averaging over prohibited variables, taking the most favorable prediction over prohibited variables (a novel method), and jointly minimizing errors as well as the association between predictions and prohibited variables. De-biasing can recover some of the original decisions, but the results are sensitive to whether the bias is effected through a proxy.

Large language models (LLMs) are revolutionizing various fields by leveraging large text corpora for context-aware intelligence. Due to the context size, however, encoding an entire graph with LLMs is fundamentally limited. This paper explores how to better integrate graph data with LLMs and presents a novel approach using various encoding modalities (e.g., text, image, and motif) and approximation of global connectivity of a graph using different prompting methods to enhance LLMs' effectiveness in handling complex graph structures. The study also introduces GraphTMI, a new benchmark for evaluating LLMs in graph structure analysis, focusing on factors such as homophily, motif presence, and graph difficulty. Key findings reveal that image modality, supported by advanced vision-language models like GPT-4V, is more effective than text in managing token limits while retaining critical information. The research also examines the influence of different factors on each encoding modality's performance. This study highlights the current limitations and charts future directions for LLMs in graph understanding and reasoning tasks.

When it comes to artificial Intelligence and machine learning, most often we hear them discussed in a clinical context. But that's not the only realm where AI and ML could make an impact. In fact, revenue cycle is well-suited to AI and MI. According to Nick Giannasi, chief AI officer for Change Healthcare, it's almost the perfect problem for AI and ML to solve. "You basically have a lot of historical transactions, basically claims, and then information that comes back: what was paid, what wasn't paid, what was the reason. When you have that in large volumes, it's really hard to represent that with human knowledge or rules because there are lots of combinations," Giannasi said.