A Frank System for Co-Evolutionary Hybrid Decision-Making
Federico Mazzoni, Riccardo Guidotti, Alessio Malizia
TL;DR
The paper tackles high-stakes decision support by enabling human-in-the-loop hybrid decision-making where a model learns in parallel with user labeling. It introduces Frank, a co-evolutionary HDM that uses an Incremental Learning EFDT model and a suite of checks (IRC, IFC, SLC, GFC) plus supervisor rules and fairness constraints, with explainability and bad-faith safeguards. Through simulations on Adult, COMPAS, and HR datasets across diverse user types, Frank shows improvements in accuracy and fairness, and explanations increase user acceptance under skepticism. Limitations include focus on tabular data and low-dimensionality, with future work toward other data modalities and deeper trust dynamics or integration with Learning-to-Defer.
Abstract
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.