Detecting and explaining unfairness in consumer contracts through memory networks

Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the classification accuracy, but are also able to offer meaningful, natural language explanations of otherwise opaque classifier outcomes

The human hand and foot in evolution and art : the effects of wearing footwear

The structures of the human hands and feet are shaped by evolution and its effects on the brain, skeleton and other structures, and on behavior. We used measurements obtained of hands and feet from living humans in Europe, the Americas (South and North) and Australia and images of hands and feet in cave art, paintings, and photographs obtained from the Web including some from Africa. We used the ratios of the third finger/width of hand and second toe/width of foot. We hypothesized that hand ratios would not have changed over millennia whereas, because of the use of footwear and mechanical locomotion, the ratios obtained from feet could have changed significantly. Here we report that statistical analyses and modeling confirmed our initial hypothesi