Enabling employees to work as citizen data scientists has many benefits—if agencies take the right precautions, according to a recent paper.
The idea of a citizen data scientist—a person who creates analytic models such as artificial intelligence (AI) and machine learning (ML)—makes sense, said Reid Blackman, who coauthored “The Risks of Empowering ‘Citizen Data Scientists’” with Tamara Snipes, chief data scientist at UnitedHealthcare. Harvard Business Review published the paper in December 2022…
Public-sector use cases for what the paper terms auto-ML, or “software that provides methods and processes for creating machine learning code,” include tax fraud detection, facial recognition to track down missing children and uncovering red flags that could lead law enforcement to further investigate and thwart terrorist activities, said Blackman, founder and CEO of Virtue, an ethical risk consultancy.
But the paper identifies three main problems associated with citizen data scientists using auto-ML. The first is that the technology “does not solve for gaps in expertise, training and experience, thus increasing the probability of failure,” according to the paper. For example, a dataset containing few instances of suspicious transactions must be sampled carefully to be usable as training data—something that “sits squarely in the expertise of the experienced data scientist.” … Read the full article here.