The implementation of Artificial Intelligence (AI) in sensitive domains requires that models are fair and clearly understandable for stakeholders to trust in the decisions made. While Explainable AI (XAI) methods approximate the behavior of models, Interpretable AI (IAI) focuses on inherently transparent representations such as decision trees. However, interpretability alone is not enough, as models may encode biases from different sources, including biases present in data, leading to unfair outcomes. This introduces a fundamental trade-off between predictive performance and fairness. In this paper, we propose a novel framework that integrates evolved decision trees generated from random seed-based vectors and Quality Diversity (QD) optimization to jointly address decision interpretability, predictive performance, and fairness. Using evolutionary search, the method generates a repertoire of high-performing, interpretable models that span different regions of the fairness-accuracy space. This enables a systematic characterization of trade-offs and provides decision-makers with multiple transparent alternatives. Experimental results show that the proposed approach effectively discovers diverse decision tree models with competitive overall accuracy while achieving improved fairness across standard benchmarks.
Catalina M. Jaramillo, M. Charity, Julian Togelius· Proceedings of the Genetic a...· 0 citations
This work introduces an Evolutionary Language Model that searches over natural-language policy descriptions and compiles typed programs for execution and shows that language can serve as a steerable, execution-grounded search representation over executable program space.
Matthew Siper, A. Khalifa, Julian Togelius· 0 citations
Large language models can generate executable programs, which makes it possible to search directly over procedural content generators rather than individual levels. We study this approach in Sokoban, Zelda, Dangerous Dave, and Lode Runner. Each run evolves complete Python generators through language-model mutation and crossover. We introduce Continual Abstraction Discovery, or CAD, which extracts reusable primitives from high-fitness programs into a run-specific helper module. A 2x2 experiment crosses CAD with access to a fixed hand-written domain API. The completed data set contains 160 complete runs, with at least ten 50-generation runs in every cell. CAD raises mean final best fitness in all eight domain and API comparisons. Across all CAD runs, learned libraries are adopted by most later programs and repeatedly rediscover validation, reachability, and structural utilities. These results support that discovering reusable primitives improves evolutionary program search for content generators.
Matthew Siper, A. Khalifa, Julian Togelius· 0 citations