Machine Learning in Investigations
The Pulitzer Center supports stories that use advanced data mining techniques, such as machine learning and natural language processing, to solve a data or reporting problem related to a journalistic investigation.
Recent grantees have used machine learning to reveal the true scope of oil-well abandonment in Texas; hold land banks accountable in Ohio; and map the proliferation of gold mines in the Amazon rainforest.
These projects harnessed machine learning to augment the reporters’ capacity to tackle big data and systemic issues. The reporters combined the use of machine learning with geospatial analysis, satellite imagery, and traditional shoe-leather reporting, among other approaches.
Another interesting characteristic of the projects we have supported so far is that they all involved collaborative work, whether across newsrooms or disciplines. We encourage journalists to seek smart partnerships that can complement their skills and perspectives.
We're seeking compelling data-driven storytelling—based on original and transparent data collection and analysis—that has the potential to shape public discourse and hold the powerful accountable.