Abstract

Data plays a critical role in machine learning. Every machine learning model is trained and evaluated using data, quite often in the form of static datasets. The characteristics of these datasets fundamentally influence a model’s behavior: a model is unlikely to perform well in the wild if its deployment context does not match its training or evaluation datasets, or if these datasets reflect unwanted societal biases. Mismatches like this can have especially severe consequences when machine learning models are used in high-stakes domains, such as criminal justice (Garvie et al. 2016; Systems 2017; Andrews et al. 2006), hiring (Mann and O’Neil 2016), critical infrastructure (O’Connor 2017; Chui 2017), and finance (Lin 2012). Even in other domains, mismatches may lead to loss of revenue or public relations setbacks. Of particular concern are recent examples showing that machine learning models can reproduce or amplify unwanted societal biases reflected in training datasets (Buolamwini and Gebru 2018; Dastin 2018; Bolukbasi et al. 2016). For these and other reasons, the World Economic Forum suggests that all entities should document the provenance, creation, and use of machine learning