Sails Lunch Time Seminar Matt Young
- Monday 26 September 2022
- Online only
The Equity Implications of Representative Bureaucracy for Machine Learning in Public Administration
This project investigates the consequences of implementation choices when using machine learning (ML) to automate public sector decision making. ML has proven to be extremely susceptible to artifacts in training data that introduce bias and lead to suboptimal decision output. Public sector organizations have a responsibility to avoid making biased decisions, both because of their mandate to protect individual rights, and because of the power that they wield over people’s lives. Previous work on representative bureaucracy finds that administrators are more likely to treat individuals fairly when they share characteristics with the population they serve. Thus, all else equal, administrative data generated under conditions of active representation should contain less embedded bias, improving ML performance when used as training data. We propose a 3x3 treatment-control design to test different ML architectures trained on administrative data previously employed to identify and estimate the effect of active representation in the context of education policy on future automated decision-making.
The SAILS Lunch Time Seminar is an online event, but it is not publicly accessible in real-time. If you would like to join this seminar, please send an email to email@example.com to receive a link.