Objective: In public health, access to research literature is critical to informing decision making and to identify knowledge gaps. However, identifying relevant research is not a straightforward task since public health interventions are often complex, can have positive and negative impacts on health inequalities and are applied in diverse and rapidly evolving settings. We developed a ‘living’ database of public health research literature to facilitate access to this information using Natural Language Processing tools. Materials and Methods: Classifiers were identified to identify the study design (e.g. cohort study or clinical trial) and relationship to factors that may be relevant to inequalities using the PROGRESS-Plus classification scheme. Training data was obtained from existing MEDLINE labels and from a set of systematic reviews in which studies were annotated with PROGRESS-Plus categories. Results: Evaluation of the classifiers showed that the study type classifier achieved average precision and recall of 0.803 and 0.930 respectively. The PROGRESS-Plus classification proved more challenging with average precision and recall of 0.608 and 0.534. The FAIR database uses information provided by these classifiers to facilitate access to inequality-related public health literature. Discussion: Previous work on automation of evidence synthesis has focussed on clinical areas rather than public health, despite the need being arguably greater. Conclusion: The development of the FAIR databased demonstrates that it is possible to create a publicly accessible and regularly updated database of public health research literature focused on inequalities. The database is freely available (https://eppi.ioe.ac.uk/eppi-vis/Fair).

1978 papers that had been included in systematic reviews previously were identified for training and testing the machine learning model. Please see the paper and website for further information.