Be Fair! Can Machine Learning Engineering Agents Adhere to Fairness Constraints?

2026-06-03

AI Summary

This study addresses a critical gap in machine learning engineering (MLE) agents: the lack of built-in safeguards for ethical constraints such as fairness, which poses compliance risks in sensitive applications. For the first time, fairness is formally integrated as a core responsibility constraint into the evaluation framework for MLE agents. The authors systematically assess fairness performance across demographic groups using a natural language–driven agent on a skin-tone–stratified melanoma classification task. Experimental results reveal that models autonomously generated by current MLE agents significantly underperform human-designed baselines in both predictive accuracy and fairness, while also exhibiting high variance. These findings highlight a fundamental deficiency in existing automated modeling approaches—the absence of mechanisms to jointly optimize fairness and performance—rendering them unsuitable for responsibility-sensitive domains.

Abstract

Machine learning engineering (MLE) agents promise to automate end-to-end ML pipeline development from raw data and natural language instructions, potentially making ML accessible to non-technical domain experts. However, in sensitive and regulated domains, this abstraction creates a responsibility gap: end-users may lack visibility into design choices that affect correctness, robustness, fairness, and regulatory compliance. We argue that existing benchmarks are insufficient to assess whether MLE agents can be safely applied in such settings. We propose desiderata for a responsibility-centered evaluation framework and conduct an exploratory study on melanoma classification, focusing on fairness across skin tones as a responsibility constraint. When evaluating two recent MLE agents, we find that agent-generated pipelines show high variance and consistently underperform manually designed baselines in both predictive quality and fairness, despite fairness-oriented prompts. These preliminary results suggest that further research is needed towards redesigning MLE agents to allow humans to guide the search process and reliably assess the compliance and quality of the generated ML pipelines.

Problem

• Machine Learning Engineering Agents
• Fairness Constraints
• Responsibility Gap
• Regulatory Compliance
• Automated ML Pipeline

Innovation

• machine learning engineering agents
• fairness constraints
• responsibility-centered evaluation
• automated ML pipeline
• algorithmic fairness