Picto Web is a multi-tenant web-based tool for complex model exploration. It can transform different types of models into a variety of transient web-based views in formats such as HTML, Graphviz and PlantUML using rule-based model-to-text transformations. Picto Web implements a lazy view computation approach to support large models and complex transformations efficiently, and includes model and transformation template monitoring and push notification facilities to automatically recompute views when either are modified and deliver updated views to clients. The tool is packaged as a Docker container for ease of deployment.

Models produced by machine learning are not guaranteed to be free from bias, particularly when trained and tested with data produced in discriminatory environments. The bias can be unethical, mainly when the data contains sensitive attributes, such as sex, race, age, etc. Some approaches have contributed to mitigating such biases by providing bias metrics and mitigation algorithms. The challenge is users have to implement their code in general/statistical programming languages, which can be demanding for users with little programming and fairness in machine learning experience. We present FairML, a model-based approach to facilitate bias measurement and mitigation with reduced software development effort. Our evaluation shows that FairML requires fewer lines of code to produce comparable measurement values to the ones produced by the baseline code.