The massive progress of machine learning has seen its application over a variety domains in the past decade. But how do we develop systematic, scalable and modular strategy to validate machine-learning systems? We present, best our knowledge, first approach, which provides systematic test framework for systems that accepts grammar-based inputs. Our <sc xmlns:mml="http://www.w3.org/1998/Math/Mat...

In recent years, research into and concern over algorithmic fairness bias in machine learning systems has grown significantly. It is vital to make sure that these are fair, impartial, do not support discrimination or social injustices since algorithms becoming more prevalent decision-making processes across a variety of disciplines. This abstract gives general explanation the idea fairness, dif...

The prediction of behavior in dynamical systems, is frequently associated to the design models. When a time series obtained from observing system available, task can be performed by designing model these observations without additional assumptions or assuming preconceived structure model, with help information about system. In second case, it question adequately combining theory and subsequentl...

Although investing in environmental, social, and governance (ESG)-driven portfolios is already a large growing portion of global assets under management, applications quantitative techniques to improve standardize ESG scoring the construction are underutilized. In this article, authors propose an approach automatically convert unstructured text data into scores by using latest advances deep lea...

Concerns around machine learning’s societal impacts have led to proposals certify some systems. While prominent governance efforts date center networking standards bodies such as the Institute of Electrical and Electronics Engineers (IEEE), we argue that learning certification should build on structures from sustainability domain. Policy challenges share significant structural similarities, inc...