AI RESEARCH
Be Fair! Can Machine Learning Engineering Agents Adhere to Fairness Constraints?
arXiv CS.LG
•
ArXi:2606.04971v1 Announce Type: new 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.