A Review of Legal Precedents and Industry Expectations for Integrating Machine Learning in Safety and Engineering

Despite widespread interest in machine learning (ML), engineers and stakeholders remain uncertain about the legal and regulatory frameworks governing ML. This gap remains unaddressed as an engineer’s decision-making process, typically governed by professional ethics and practical guidelines, now intersects with complex algorithmic outputs. To bridge this gap, this narrative doctrinal review explores how engineers can navigate legal principles and legislative justifications that support and/or contest the deployment of ML technologies by synthesizing leading case law, statutes, and standards selected for their controlling and illustrative authority. Drawing on recent precedents gained from other fields, this paper argues that analogical reasoning can provide a basis for embedding ML within existing engineering codes while maintaining professional accountability and meeting safety requirements. In exploring these issues, the discussion focuses on established liability doctrines and highlights how courts have evaluated the use of predictive models. We further analyze how legislative bodies and standard-setting organizations can furnish explicit guidance equivalent to prior endorsements of emergent technologies. This exploration stresses the vitality of understanding the interplay between technical justifications and legal precedents for shaping an informed stance on ML’s legitimacy.