# The Engineer's Dilemma: A Review of Establishing a Legal Framework for Integrating Machine Learning in Construction by Navigating Precedents and Industry Expectations

arXiv: https://arxiv.org/abs/2507.08908 · HTML: https://mlsanity.com/paper/2507.08908/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 118 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 15 of 15)

- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Crafting Adversarial Input Sequences for Recurrent Neural Networks](https://mlsanity.com/paper/1604.08275/) (2016)
- [Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance](https://mlsanity.com/paper/2206.04737/) (2022)
- [On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products](https://mlsanity.com/paper/1610.01256/) (2016)
- [On Testing Machine Learning Programs](https://mlsanity.com/paper/1812.02257/) (2018)
- [How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review](https://mlsanity.com/paper/2107.12045/) (2021)
- [Technology Readiness Levels for AI & ML](https://mlsanity.com/paper/2006.12497/) (2020)
- [Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning](https://mlsanity.com/paper/2008.10740/) (2020)
- [The European AI Liability Directives -- Critique of a Half-Hearted Approach and Lessons for the Future](https://mlsanity.com/paper/2211.13960/) (2022)
- [Non-functional Requirements for Machine Learning: Understanding Current Use and Challenges in Industry](https://mlsanity.com/paper/2109.00872/) (2021)
- [Adaptive machine learning strategies for network calibration of IoT smart air quality monitoring devices](https://mlsanity.com/paper/2003.12011/) (2020)
- [Smart Automotive Technology Adherence to the Law: (De)Constructing Road Rules for Autonomous System Development, Verification and Safety](https://mlsanity.com/paper/2109.02956/) (2021)
- [Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical Sciences](https://mlsanity.com/paper/2402.00347/) (2024)

## Abstract (arXiv)

Despite the widespread interest in machine learning (ML), the engineering industry has not yet fully adopted ML-based methods, which has left engineers and stakeholders uncertain about the legal and regulatory frameworks that govern their decisions. 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 paper explores how engineers can navigate legal principles and legislative justifications that support and/or contest the deployment of ML technologies. Drawing on recent precedents and experiences 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, such as negligence and product liability, 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 in engineering practice. Finally, our analysis catalyzes a legal framework for integrating ML through which stakeholders can critically assess the responsibilities, liabilities, and benefits inherent in ML-driven engineering solutions.