A Frank System for Co-Evolutionary Hybrid Decision-Making
This work addresses labeling challenges for users needing decision support, but it appears incremental as it builds on existing human-in-the-loop approaches with added features.
The authors tackled the problem of labeling unlabeled datasets by introducing Frank, a human-in-the-loop system for co-evolutionary hybrid decision-making, which improved accuracy and fairness in decisions through user simulations.
We introduce Frank, a human-in-the-loop system for co-evolutionary hybrid decision-making aiding the user to label records from an un-labeled dataset. Frank employs incremental learning to ``evolve'' in parallel with the user's decisions, by training an interpretable machine learning model on the records labeled by the user. Furthermore, Frank advances state-of-the-art approaches by offering inconsistency controls, explanations, fairness checks, and bad-faith safeguards simultaneously. We evaluate our proposal by simulating the users' behavior with various levels of expertise and reliance on Frank's suggestions. The experiments show that Frank's intervention leads to improvements in the accuracy and the fairness of the decisions.