Most representative decision tree ensemble methods have been used to examine the variable importance of Treasury term spreads to predict US economic recessions with a balance of generating rules for US economic recession detection. A strategy is proposed for training the classifiers with Treasury term spreads data and the results are compared in order to select the best model for interpretability. We also discuss the use of SHapley Additive exPlanations (SHAP) framework to understand US recession forecasts by analyzing feature importance. Consistently with the existing literature we find the most relevant Treasury term spreads for predicting US economic recession and a methodology for detecting relevant rules for economic recession detection. In this case, the most relevant term spread found is 3 month to 6 month, which is proposed to be monitored by economic authorities. Finally, the methodology detected rules with high lift on predicting economic recession that can be used by these entities for this propose. This latter result stands in contrast to a growing body of literature demonstrating that machine learning methods are useful for interpretation comparing many alternative algorithms and we discuss the interpretation for our result and propose further research lines aligned with this work.

My life has always involved centering the voices of those historically marginalized in order to foster equitable communities. Growing up, I lived in a small suburb just outside of Cleveland, Ohio and I was fortunate enough to attend Laurel School, an all-girls school focused on encouraging young women to think critically and solve difficult world problems. But my lived experience at school was so different from kids who lived even on my same street. I was grappling with watching families around me contend with an economic recession, losing any financial security that they had and I wanted to do everything I could to change that. Even though my favorite courses at the time were engineering and African American literature, I was encouraged to pursue economics.

The Covid-19 pandemic has raised lots of challenges for those working in project management, but the UK's GDP fell by 20.4% in the first two quarters of 2020, therefore potentially there are more difficulties on the horizon. How will this environment affect project delivery and can AI solutions help mitigate some of the challenges?

In 2019, 53% of global data and analysis established by decision-makers announced that artificial intelligence is established, or in full development within their company. Here are the artificial intelligence forecasts for 2020. It is important to note that these findings are taken from statistics revealing percentages calculated from the observation of Fortune 500 companies. The Fortune 500 companies are recognized as the most profitable in the United States. The study shows finding that 29% of developers have worked on AI and machine learning in recent years. The findings have come from a Forrester study.