AI arrives in the Courts
1. The shift is already underway
Courts are adopting artificial intelligence less as a single “modernisation program” than as a series of incremental tool decisions: a triage widget here, an automated form there, a transcription contract, a translation feature, an analytics dashboard. Each tool appears operational. Collectively, they change how public authority is exercised.
This shift is often misunderstood as a technology upgrade. It is more accurately a change in institutional operating form. When AI tools are embedded into intake pathways, scheduling, evidentiary handling, prioritisation, or drafting, they shape what is seen, what is asked, what is delayed, and what is treated as decisive. That is a governance problem before it is an engineering problem.
The central question is therefore not whether courts should use AI. It is how to ensure that when they do, authority remains legible and accountable—to parties, to judges, to the public, and to the legal system’s own standards of procedural fairness.
2. A practical taxonomy: what “AI in courts” actually means
Court AI is best understood as a set of distinct tool types, each with different risk profiles and governance requirements.
(a) Administrative automation
Tools that reduce friction without materially shaping outcomes:
smart forms and guided filing
document classification and routing
transcription and translation support
scheduling optimisation
duplicate detection and basic fraud flags
These tools can still cause harm—particularly through exclusion, accessibility failures, or systematic error—but their influence is primarily on process.
(b) Decision support
Tools that influence prioritisation, resource allocation, or the framing of decisions:
triage and suitability screening
risk/priority scoring
settlement likelihood or “case complexity” estimates
prompts that suggest next procedural steps
Decision support is where automation bias becomes a structural risk. Even if the final decision-maker is human, the tool can strongly shape the path taken.
(c) Drafting and synthesis
Tools that produce text that may be relied upon:
summarising submissions and evidence
generating draft reasons, orders, or correspondence
producing “issues lists” or proposed findings
Drafting tools can be safe in bounded settings, but they require controls against plausible-sounding error and false completeness.
(d) Integrity and compliance tooling
Tools that enforce rules:
identity verification and liveness checks
anomaly detection (repeat litigants, templated claims, suspected fabrication)
document provenance and chain-of-custody workflows
monitoring for prohibited content or coercion indicators
These tools can protect courts, but can also embed contested assumptions about risk, credibility, and normality.
This taxonomy matters because “AI” is not a single governance object. Courts need differentiated guardrails that match the authority a tool effectively exercises.
3. The governance test: how much authority does the tool exercise?
A useful governance test is simple:
What authority does this tool exercise in practice, not in theory?
Four dimensions help locate that authority:
Outcome influence: Does it affect merits or only administration?
Reversibility: Can an individual easily correct or contest the tool’s output?
Opacity: Can parties and supervisors see why the tool produced its result?
Scale: Does an error affect one case, or produce systemic skew across thousands?
A scheduling optimiser that shifts hearing dates is not the same as a triage model that filters claims as “unsuitable”, or a drafting tool that produces reasons that a judge edits under time pressure. Governance must be calibrated accordingly.
4. Why generative tools require special restraint
Generative systems bring distinctive benefits: they interpret natural language narratives, produce plain-English explanations, generate drafts at scale, and reduce administrative load. For courts, that can translate into improved access for self-represented litigants and lower rates of defective filings.
But generative systems also bring distinctive failure modes. In small-claims triage testing, it is possible for an assistant to identify suitability correctly in some matters yet miss determinative defects (for example, capacity of a proposed respondent or an expired limitation period), while still producing text that appears coherent and complete.
These are not edge cases; they are characteristic errors of probabilistic text generation in high-stakes procedural settings:
False completeness: fluent answers that omit critical legal conditions.
Context drift: attention weights can overvalue recent/salient details, weakening chronology handling and multi-factor tests.
Domain mismatch: narrative plausibility can be mistaken for legal sufficiency.
Silent failure: the tool rarely signals uncertainty in a way lay users can interpret.
The lesson is not “generative AI is unusable.” The lesson is that courts must design for bounded use, structured outputs, and contestability by default.
5. Six practical examples—and what governance looks like in each
The following examples are intentionally concrete. Each identifies (i) what the tool does, (ii) what can go wrong, and (iii) the minimum governance conditions that preserve legitimacy.
Example 1: Guided filing assistant for self-represented litigants
Tool function: Converts a narrative into structured fields; flags missing documents; proposes next steps.
Failure mode: The assistant misclassifies the dispute type, fails to identify jurisdictional thresholds, or discourages a valid claim due to overconfident generalisation.
Minimum governance:
“No-train, no-retain” by default for user content, with court-controlled hosting or an equivalent regime that prevents reuse of personal data for vendor training. Nicolas PATRICK M4U2 Assignment…
Clear separation between information and advice: explain the process, not the merits.
Structured prompts and constrained outputs: the model should fill templates, not improvise doctrine.
Escalation triggers: if the user indicates family violence, vulnerability, language barriers, or urgency, the pathway must route to human assistance.
Auditability: logging that enables later review of what the tool asked and what it suggested.
Example 2: Triage/suitability screening for claims
Tool function: Flags defective filings; suggests dismissal pathways for out-of-time or clearly non-justiciable claims.
Failure mode: Systematic filtering of valid but atypical claims; automation bias leading staff to treat flags as decisions.
Minimum governance:
Tool outputs must be advisory, never dispositive, unless authorised through explicit policy and rule change.
Two-channel review: triage flags require a human confirmation step and a reason code.
Disclosure model: parties should be told when a triage tool materially affected routing or timing, at least at a category level.
Performance monitoring: track false negatives/positives and error distribution across protected or disadvantaged groups.
Contestability: a simple mechanism to request human reconsideration.
Example 3: Transcription and translation support
Tool function: Produces draft transcripts; supports interpretation and accessibility.
Failure mode: Subtle translation or transcription errors that distort meaning; uneven performance across accents, dialects, or code-switching.
Minimum governance:
Tiered reliability thresholds: define when human review is mandatory (e.g., contested evidence, credibility issues, or sentencing).
Correction protocol: parties can propose corrections with a structured process.
Provenance markers: transcripts clearly label machine-generated sections and confidence.
Equality testing: evaluate performance across language groups and speech patterns, not only averages.
Example 4: Scheduling optimisation and resource allocation
Tool function: Allocates hearing dates, prioritises lists, manages backlogs.
Failure mode: Optimisation can encode value judgements—e.g., prioritising throughput over fairness, or disadvantaging those who need interpreters, accommodations, or remote participation.
Minimum governance:
Published scheduling principles (what the optimiser is trying to maximise and what it is constrained from doing).
Fairness constraints embedded into optimisation objectives (not added as afterthoughts).
Review rights for parties where listing materially affects outcomes.
Transparency to leadership: regular reporting on distributional effects.
Example 5: Decision-support scoring (risk/priority/complexity)
Tool function: Produces scores that guide sequencing, supervision intensity, or intervention.
Failure mode: Scores become de facto decisions; staff defer even when they disagree; the system inherits historical bias in training data.
Minimum governance:
A “human reasons” requirement: if the score is followed, record why; if overridden, record why. This keeps human responsibility real.
Model cards for governance, not marketing: what data, what limits, known failure conditions.
Ongoing drift testing: courts are not stable environments; data and behaviour change.
Separation of policy and prediction: the score may predict, but policy decides what the court does with it.
Example 6: Generative drafting assistant for reasons and orders
Tool function: Drafts summaries, issues lists, and initial text for reasons or orders.
Failure mode: Hallucinated facts; missing evidentiary bases; “plausible legal language” masking error; subtle citation and quotation mistakes.
Minimum governance:
Evidence-bound drafting: the tool only drafts from provided materials, with explicit quotations and pinpoint references.
Citation locking: the tool must cite to an internal record set; if it cannot, it must not invent.
Red-flag detection: prompt for cross-check where there are dates, capacity, limitation, jurisdiction, or credibility findings.
Workflow segregation: drafting occurs in a controlled environment, with no external leakage and full logging.
6. Procurement is governance (whether acknowledged or not)
For courts, the most consequential decisions often sit in vendor contracts and implementation choices, not in formal AI policy.
Key procurement terms are governance terms:
Data rights and retention (including strict no-retain regimes where appropriate)
Audit rights (access to logs, performance metrics, and model update history)
Update control (when models change, who approves, and how impacts are tested)
Explainability and transparency commitments
Incident response (who is notified, timelines, rollback capacity)
Lock-in constraints (portability, open standards where feasible, exit planning)
Courts should treat these as non-negotiable design features of legitimacy, not as “commercial details”.
7. The institutional minimum: principles that scale
Court AI governance does not need to begin with comprehensive ethical frameworks. It can begin with a few institutional minimums that are operationally implementable.
Authority must remain identifiable. People must be able to tell who decided, on what basis, and with what accountability.
Contestability must be built in. AI-supported pathways should default to easy escalation and human reconsideration.
Discretion must be disciplined, not displaced. Tools can inform, but they must not substitute for accountable judgement without explicit authorisation.
Transparency should match impact. The greater the tool’s practical influence, the stronger the disclosure and review requirements.
Security and privacy are prerequisites, not add-ons. Court user data is uniquely sensitive and should be treated as such through architecture and contractual controls. Nicolas PATRICK M4U2 Assignment…
Evaluation is continuous. Courts should plan for drift, feedback loops, and changing user behaviour from day one.
8. The direction of travel: from tools to pathways
Over time, the governance challenge shifts. Early adoption is tool-by-tool. Mature adoption becomes pathway-based: integrated sequences of triage, guidance, filing, negotiation, adjudication, enforcement, and review.
At that point, the core risk is not any single model. It is the emergence of an unseen procedural architecture—a system of nudges, defaults, and automated routings that can shape justice outcomes without clear visibility.
Courts can modernise without losing legitimacy. But only if they treat AI not as a layer added to an existing institution, but as a design force that changes institutional form. Governance, in this context, is not merely oversight. It is the act of deciding, explicitly and transparently, where authority sits as technology changes the operating environment.
Footnote
Testing performed by Nicolas Patrick, student of AI for Business Transformation, Saïd Business School, University of Oxford (2025).