Engineering Flaws

Whitepaper -  Legal Implications of AI Scientific and Engineering Challenges

This section focuses on the specific scientific and engineering limitations of current AI technology that, when translated into real-world failures, form the technical basis for legal claims and liability. These issues move beyond mere operational failures to target the fundamental trustworthiness of the underlying algorithms.
 

Model Robustness and Adversarial Attacks.

An AI system is legally expected to perform reliably under normal operating conditions. However, many state-of-the-art models are susceptible to adversarial attacks, subtly altered inputs, often imperceptible to humans, that cause a model to output an incorrect, and potentially dangerous, decision e.g., confusing a stop sign with a yield sign.

Litigation can arise from system failures where the defense is "the system was hacked" or "the input was poisoned." The plaintiff’s counter argument, often successful, is that the developers failed to meet the legal duty to build a reasonably robust model against known attack vectors. 

The scientific community has documented these vulnerabilities extensively; failure to implement defense mechanisms, like adversarial training, can therefore be characterized as technical negligence, making the engineering challenge of robust design a direct legal liability.

Data Lineage and Provenance.

As detailed in the IP and legacy systems sections, a defendant must be able to prove, with scientific certainty, exactly what data was used to train a model and how that data was processed and weighted. This requires maintaining an immutable, auditable "AI Bill of Materials". Technically, this is complex due to the massive scale and continuous iteration of training data sets. Legally, failure to produce clear data provenance can be fatal. For instance, in a bias claim, if the defendant cannot trace the dataset to prove that protected class features were excluded or handled correctly, the claim of non-discriminatory design is undermined. In copyright litigation, provenance is essential to prove non-infringement or trace the origin of a potentially infringing output. The inability to scientifically reproduce a model’s decision trail compromises the entire legal defense, leading to evidentiary challenges and potentially adverse rulings.

Model Drift and Deterioration 

AI models, especially those operating in dynamic environments, do not remain static; their performance degrades, or drifts, over time as real-world data distributions change. This drift can lead to new, unintended, and harmful biases or a drop in accuracy that causes injury. The legal duty to monitor and periodically retrain is an emerging area of negligence. Litigation can hinge on expert witness testimony establishing when the performance decline crossed a recognized critical threshold (a technical metric) and whether the organization’s failure to intervene at that point constitutes negligence or a breach of professional duty. This shifts the legal focus from the initial design to the ongoing, operational maintenance life cycle of the AI system, creating a continuous legal obligation.

Reproducibility and Auditability

Unlike traditional software, the output of a deep learning model is often highly sensitive to initial conditions, random seeds, and the exact computational environment. For a court to allow an expert witness to testify on the function of an AI, that expert must often be able to reproduce the decision that caused harm. When technical limitations prevent this, because the exact training run cannot be recreated, it raises serious due process and discovery concerns. This forces legal teams to mandate engineering solutions, such as deterministic algorithms and rigorous version control, not merely for good engineering practice, but for mandatory legal defensibility.

DISCUSSION AND CASE LAW

AI internal technical issues and challenges such as algorithmic bias, lack of transparency, and insufficient explainability, can lead to legal liabilities under anti-discrimination, privacy, and product liability laws. These issues arise when AI systems process data in ways that inadvertently perpetuate discrimination, obscure decision-making processes, or fail to provide accountable outcomes. For instance, algorithmic bias occurs when training data reflects historical prejudices, resulting in disparate impacts on protected groups, such as in hiring or lending decisions. 

The Institute for Responsible AI emphasizes the need for alignment with human values, fairness, and transparency to mitigate these risks and build public trust, particularly in sectors like healthcare and criminal justice where technical liabilities can have profound societal effects. Legal frameworks, including the U.S. Civil Rights Act of 1964 and the Equal Credit Opportunity Act, increasingly hold organizations accountable for such flaws, requiring proactive measures like bias audits and impact assessments to prevent harm.
 

Case law illustrates how courts and regulators address these technical shortcomings. In State v. Loomis (2016), the Wisconsin Supreme Court examined the use of the COMPAS risk assessment tool in criminal sentencing, ruling that its potential biases must be disclosed to avoid violating due process, though the tool could still be used if not the sole basis for decisions. This case remains a foundational precedent for demanding transparency in opaque AI systems used in high-stakes decisions.
 

In employment contexts, the U.S. Equal Employment Opportunity Commission (EEOC) has pursued enforcement actions against discriminatory AI tools. In August 2023, the EEOC settled its first AI-specific employment discrimination case, EEOC v. iTutorGroup, for $365,000, where the company's recruitment software automatically rejected female applicants over age 55 in violation of the Age Discrimination in Employment Act (ADEA). This settlement highlighted direct liability for age-based algorithmic exclusion.

A landmark ongoing case is Mobley v. Workday, Inc. (filed 2023, N.D. Cal.). Plaintiff Derek Mobley alleged that Workday's AI-powered applicant screening tools discriminated based on race, age, and disability under Title VII, the ADEA, and the ADA by relying on biased training data and employer preferences. In July 2024, the court denied Workday's motion to dismiss on an "agent" theory of liability, holding that Workday could be directly liable as an agent of employers for delegating traditional hiring functions to its AI tools. The EEOC filed an amicus brief supporting this approach in April 2024. By May 2025, the court granted conditional certification of ADEA claims as a nationwide collective action, marking a significant step in holding AI vendors accountable and potentially affecting millions of applicants.

Other notable developments include complaints and scrutiny against vendors like HireVue for video analysis potentially discriminating against individuals with disabilities, leading to changes in practices, and Amazon's 2018 decision to scrap its internal AI recruiting tool after it showed gender bias by downgrading resumes with female-associated terms.

In lending and insurance, claims of AI-driven disparate impact continue to advance. In Wynn v. State Farm (ongoing as of 2025), plaintiffs alleged that State Farm's algorithmic claims-processing tools discriminated against Black policyholders through longer wait times and greater scrutiny, with a 2023 court ruling allowing the disparate impact claim to proceed based on statistical disparities and the known propensity of machine-learning antifraud algorithms to embed bias.
 

The Apple Card controversy (2019) prompted investigations into gender-based credit limit disparities, reinforcing explainability needs under fair lending laws. The Consumer Financial Protection Bureau (CFPB) has addressed related discriminatory practices, as in the 2020 Townstone Financial case involving marketing that could extend to AI decisions violating the Equal Credit Opportunity Act.
 

In housing, the 2018 HUD settlement with Facebook required modifications to its ad-targeting algorithms to prevent discrimination based on protected characteristics, enforcing transparency in AI-driven systems.

These cases reflect an evolving judicial and regulatory trend toward greater accountability for AI developers and deployers, including vendor liability under agency theories, mandatory disclosures, and bias mitigation. They align with the Institute for Responsible AI's advocacy for robust governance, ethical practices, and international standards to address cross-border technical risks. Businesses must implement regular audits, explainability mechanisms, and compliance programs to reduce exposure to discrimination claims, regulatory enforcement, and reputational harm.