Lead: Ayris Narock
This team educates and develops resources encouraging the use of Responsible Machine Learning. Responsible ML includes the practice of building explainability and interpretability into a model and addressing the issue of data bias. It allows the practitioner to connect the input data to the output and trace what specific elements have the most influence on the model’s decision. Responsible ML encourages the development of models that are transparent, explainable, verifiable, robust, and repeatable.