Artificial Intelligence promises to change everything. Feed it a million
contracts, tens of thousands of delay claims, centuries of case law, case
specific evidence and arguments, and it will predict outcomes with mathematical
precision. No more waiting six months for a forensic scheduler's report. No
more paying high hourly rates for expert testimony. Just clean, fast, objective
answers.
Except there's nothing clean or objective about it.
The construction industry stands on the edge of a dangerous
misstep—adopting AI systems that promise efficiency while quietly undermining
the foundational principles of fair dispute resolution. The risk isn't that AI
will make mistakes. The risk is that it will make systematic errors look like
scientific truth, turning historical prejudices into algorithmic certainties
that nobody can challenge.
The Data Doesn't Show What Really
Happens
Every AI model learns from historical data. But in construction disputes,
the historical record is fundamentally incomplete—and that incompleteness isn't
random.
The Settlement Gap is the core problem. Industry estimates suggest 85-90% of construction
disputes settle privately or through confidential arbitration. These
resolutions never become public. What does enter the public record? The
disasters: disputes so contentious, so broken, that parties chose to endure
years of litigation rather than negotiate.
Train an AI on this data and you're not teaching it about construction
disputes. You're teaching it about construction warfare.
The model learns that delay equals litigation. That an RFI clarification
signals trouble. That any scope ambiguity leads to court. It has no exposure to
the thousands of projects where contractors and clients worked through delays
collaboratively, where variations were negotiated fairly, where disputes were
resolved with a handshake.
This creates a perverse outcome: the AI becomes the pessimist in the
room, interpreting normal project friction through the lens of catastrophic
failure. A subcontractor's legitimate request for a time extension gets flagged
as "high litigation risk" because it pattern-matches to the 10% of
cases that exploded into arbitration—ignoring the 90% where similar requests
were resolved amicably.
Then there's the context problem. AI models analyzing project communications rely
heavily on sentiment analysis and keyword detection. An email from a site
manager stating "I need your response by EOD or we're stopping work"
might be classified as hostile or threatening. But anyone who's worked a
construction site knows that urgency isn't hostility—it's Tuesday.
If the AI correlates "urgent tone" with "legal
liability," it creates systematic unfairness against proactive
communication. The project manager who clearly flags problems early gets
penalized. The one who stays silent until the problem becomes critical gets
rewarded.
The Human Cost: When Algorithms Anchor
Decisions
But data bias is only half the problem. The other half is what happens
when humans interact with AI predictions.
Picture this: A claims consultant receives notification of a delay claim.
Before reading the contractor's submission, before reviewing the schedule,
before checking weather records, they input the basic facts into an AI risk
assessment tool. Seconds later: "Claim Validity: 8% probability.
Recommended Action: Reject."
What happens next is predictable and dangerous.
The consultant doesn't approach the claim with an open mind. They
approach it looking for reasons to confirm the AI's prediction. This is anchoring
bias—the psychological tendency to rely heavily on the first piece of
information received. The AI's 8% assessment becomes the truth, and everything
that follows is unconsciously filtered through that lens.
An ambiguous contract clause? Interpreted unfavorably. A missing piece of
documentation? Proof of weakness, not an administrative oversight. The AI
prediction becomes a self-fulfilling prophecy.
This is compounded by automation complacency—the tendency to trust
automated systems over personal judgment, especially under time pressure. When
a sophisticated AI system delivers a definitive score, the temptation to accept
it and move on is overwhelming.
The result? Legitimate claims get rejected not because they lack merit,
but because an algorithm—trained on biased data, operating through opaque
logic—said they were probably invalid, and nobody had the time or confidence to
disagree with the machine.
The Courtroom Crisis: When AI Can't
Explain Itself
In traditional dispute resolution, expert witnesses must explain their
reasoning. A delay analyst walks through their logic: why they linked specific
activities, what evidence supports their conclusions, which methodologies they
applied. The opposing counsel can challenge every assumption. The logic is
visible and testable.
Replace that expert with an AI model that outputs "Delay
liability: Contractor 73%, Owner 27%"
and ask "Why?" The AI cannot answer in human-intelligible terms—only
by pointing to millions of weighted parameters across neural network layers.
This creates a legal crisis. Standards like Daubert require scientific
evidence to be testable with known error rates.
An AI that claims to be 99% accurate but is 0% explainable is worthless
in high-stakes disputes. Justice requires transparent reasoning that can be
challenged and understood.
The Uncomfortable Truth About Human
Experts
Before we crown AI as the villain, we need to acknowledge that
traditional expert testimony isn't perfect either. Human experts bring their
own biases—a contract manager who spent 20 years working for contractors
develops different instincts than one who worked for owners.
The difference is that human bias can be interrogated. You can ask an
expert about their career history, their assumptions, their methodology. The
bias is visible, which means it's manageable.
AI bias is insidious precisely because it appears objective. The
algorithm doesn't have a career history or personal preferences. It just has
math. And because it's math, it carries an unearned credibility—the assumption
that numbers are neutral, that probabilities are pure.
This is the core danger: AI launders historical unfairness into the
appearance of mathematical certainty.
It takes decades of systematically biased dispute outcomes and
crystallizes them into an algorithm that simply reproduces those patterns while
calling them "predictions." The model isn't discovering truth. It's
encoding injustice at scale.
The Path Forward: Glass Boxes, Not
Black Boxes
None of this means AI has no place in construction dispute resolution.
Speed matters. Efficiency matters. The ability to quickly analyze thousands of
contracts or identify relevant precedents could genuinely improve outcomes.
But the industry must demand transparency as a precondition for adoption.
Enter the "Glass Box" approach: AI systems that prioritize
explainability over raw predictive power. Instead of just generating scores,
these systems provide audit trails:
- "Risk detected based on
keyword frequency in contractor emails between March 15-April 2,
specifically references to 'critical path' (23 occurrences) and 'your
responsibility' (8 occurrences)"
- "Schedule analysis suggests
potential concurrent delay based on similarity to Case ID 4482 and Case ID
8231"
- "Contract clause 14.3
language matches high-dispute-risk patterns in 67% of comparable
projects"
This transforms AI from an invisible judge into a powerful research
assistant. It surfaces relevant information, highlights potential issues, and
draws attention to patterns—but leaves the final interpretation to humans who
can weigh context, intent, and nuance.
Currently, no major LLM is a true 'Glass Box.' However, the
industry is pivoting toward Retrieval-Augmented Generation (RAG) systems
that force opaque models to show their work by citing specific source documents
and data points—effectively placing a glass pane over the black box. Looking
further ahead, emerging architectures like Liquid Neural Networks
promise to replace the black box entirely. These adaptive systems use
interpretable mathematical equations that change dynamically based on inputs,
allowing researchers to trace exactly how the network processes information and
reaches conclusions. Unlike traditional neural networks where decision-making
happens across millions of inscrutable weighted connections, Liquid Neural
Networks offer auditable, mathematical certainty—each computational step can be
examined and verified, making them ideal candidates for applications demanding
transparent, defensible analytical processes.
The tradeoff is real, but it's a feature, not a flaw. Glass
Box systems are less computationally powerful than Black Box counterparts
because they constrain their analysis to patterns humans can trace and
verify—activating fewer neural connections and employing simpler computational
pathways to enable transparency. A deep learning Black Box can identify
correlations no human would spot, but those correlations cannot be
independently verified, tested, or challenged. When opposing counsel
cross-examines your methodology, "the AI said so" isn't defensible.
The Glass Box's transparency transforms it from an inscrutable oracle into a
defensible analytical tool where every input, assumption, and calculation can
be explained and validated. In construction disputes, the critical question
isn't whether Glass Box systems match Black Box computational power—it's
whether any conclusion, no matter how sophisticated, has value if it cannot be
explained, tested, and defended under adversarial scrutiny.
The construction industry must decide: Do we want maximum predictive
accuracy in a system nobody can challenge, or slightly lower accuracy in a
system that can be interrogated, tested, and refined?
For dispute resolution—where fairness matters as much as efficiency—the
answer must be the latter.
Conclusion: What Kind of Industry Do
We Want?
For decades, construction has struggled with adversarial relationships.
Contractors and clients eye each other with suspicion. Every variation becomes
a battle. This culture is expensive, exhausting, and increasingly
unsustainable.
Many in the industry push for integrated project delivery, collaborative
contracts, partnering approaches that emphasize shared goals over antagonistic
positions. They want construction to become less adversarial, more cooperative.
But here's the paradox: if we train AI systems on the adversarial past—on
the 10% of disputes that exploded into litigation rather than the 90% that were
resolved collaboratively—we're encoding that antagonism into the future. We're
building technology that assumes bad faith, that interprets ambiguity as
manipulation, that sees every communication through the lens of conflict.
This is the real stakes of the AI debate. It's not just about accuracy or
efficiency. It's about whether we use technology to escape the industry's
adversarial patterns or to entrench them permanently.
As the industry integrates these powerful tools, one principle must be
non-negotiable: If the AI cannot explain its reasoning to a judge, it has no
business influencing the dispute.
Justice requires sunlight. Fairness demands transparency. The future of
construction technology must be built with glass, not black boxes. The promise
of AI isn't speed for its own sake—it's speed in service of better outcomes.
But if we sacrifice transparency for efficiency, if we trade explainable
reasoning for opaque predictions, we'll achieve speed at the cost of justice.
That's not progress. That's just faster unfairness.
