CLASSIEfier: Using Machine Learning to Paint a Picture of Social Sector Trends
YOW! Data 2019
Tracking the flow of funding and other support to social sector organisations in Australia has historically been difficult because of inconsistencies in categorisation, or the absence of categorisation entirely. Our Community (Melbourne based social enterprise) developed CLASSIE to serve as a universal classification system for Australian social sector initiatives and entities. We are now developing a Machine learning algorithm to reduce or remove the need for manual (human) classification. Once released, CLASSIEfier will allow us to classify historical records on behalf of grantmakers and other social sector supporters, and reduce the need for human intervention in classification of current and future records. In a long term will allow us to answer fundamental questions such as: Where is the money going? Are we helping the areas in most need?
I will present the project scope and development of CLASSIEfier, highlighting my experiences using Machine Learning in the social sector. I will also list the difficulties of working with text and sensitive data, and the methodologies to identify and mitigate algorithmic biases.
I am an Astrophysicist by training who transitioned into Data Science in 2017. I have 7 years of research and technical experience.