About Lawluate
Govern AI-Assisted Legal Work. Verify Before You Trust.
Lawluate is an AI Governance and Risk Scoring System for Nigerian legal practice. It helps practitioners select an appropriate AI model for a legal task, complete a structured Duty of Inquiry Assessment, calculate a governance risk score, and produce a professional audit record of the verification actually performed.
All processing, storage, and PDF generation occurs entirely within your browser — no backend, no external API calls, and no transmission of your assessment data to any server.
Privacy-by-Design
All assessment data is processed and stored locally in your browser.
Browser-Only Operation
No backend, no accounts, no cloud storage, no external transmission.
Governance Focus
Assesses governance risk and verification completeness, not legal correctness.
Professional Neutrality
Does not endorse or promote any AI vendor or product.
Evidence-Informed
Model recommendations derive from a validated, version-controlled research configuration.
Human Judgment First
Every output remains subject to independent professional verification.
Why Verification Matters
AI-generated legal material may sound persuasive while containing fabricated authorities, jurisdictional errors, incomplete reasoning, outdated law, factual inaccuracies, structural bias, or unsupported conclusions. A high-confidence response is not proof of legal correctness. Lawluate therefore requires a structured Duty of Inquiry Assessment before calculating governance risk.
Professional Risks Lawluate Helps You Govern
Unverified Reliance
A fluent AI response may be accepted without checking its authorities, facts, reasoning, or jurisdiction. Lawluate requires practitioners to document independent verification before reliance.
Silent Failure
Silent Failure occurs when an AI system produces an incorrect result with sufficiently high confidence that the error may not be obvious to the practitioner. Lawluate does not automatically detect Silent Failure, but its Duty of Inquiry process is designed to reduce uncritical reliance on apparently confident outputs.
Type II Logic Failure
A Type II Logic Failure occurs when a model reaches the correct outcome but relies on materially defective, incomplete, or legally unsound reasoning. A correct final conclusion therefore does not remove the need to verify the ratio decidendi, statutory basis, supporting authorities, and factual application.
Fabricated or Misapplied Authority
AI systems may invent authorities, misstate citations, rely on overruled decisions, confuse jurisdictions, or apply a real case to a proposition it does not support. Every authority must be checked against an authorised legal source.
Domain Specificity
Model performance may differ across legal domains. Strong overall performance does not guarantee equivalent reliability in Contract, Tort, Commercial, Criminal, Procedural, Banking, Land/Mortgage, or document-hierarchy matters.
Structural Bias
AI outputs may reproduce imbalances within training data, legal reporting practices, language coverage, institutional access, and historically dominant legal perspectives. Professional review must consider whether an output overlooks relevant Nigerian legal, social, institutional, or linguistic context.
Epistemic Injustice
Epistemic injustice may arise when certain persons, communities, experiences, or legal perspectives are given less credibility or are poorly represented in the information available to an AI system. Practitioners must remain alert to whose knowledge, evidence, and interests may be missing or undervalued.
Automation Bias
Automation bias occurs when users favour a computer-generated conclusion merely because it appears systematic, detailed, or confident. Lawluate's recommendations and scores are advisory and must never displace professional judgment.
Confidentiality and Data Governance
Practitioners must consider whether confidential, privileged, personal, or client-identifiable information is appropriate for entry into any AI system. Lawluate processes its own assessment data locally, but it does not control the privacy, retention, training, or security practices of external AI products selected by the practitioner.
Evidence Basis and Public Validation
Recommendation evidence was incorporated through a validated, version-controlled research configuration. Public validation categories include Accuracy Validation, Confidence Calibration, Legal Reasoning Validation, and Implementation Validation. Detailed research methods remain proprietary.
- Evidence source
- Lawluate Primary Research and NaijaLaw Benchmark
- Benchmark scope
- Nigerian legal AI performance evaluation
- Configuration version
- 3.0.0
- Evidence reviewed
- 2026-07-28
Accuracy Validation
Evaluates whether benchmarked AI outputs align with verified legal outcomes used in the research evidence. This helps ensure that recommendation values are based on demonstrated performance rather than subjective model preference.
Confidence Calibration
Evaluates whether a model's expressed confidence is proportionate to its observed performance. This helps distinguish dependable confidence from confidently presented error and reduces overreliance on raw accuracy alone.
Legal Reasoning Validation
Evaluates whether model outputs demonstrate legally supportable reasoning rather than merely reaching a plausible conclusion. This reinforces the requirement to verify authorities, jurisdiction, reasoning, statutory basis, and factual application.
Implementation Validation
Confirms that Lawluate applies the approved evidence configuration correctly, calculates recommendation scores consistently, displays the intended evidence status, and preserves the separation between model recommendations and governance-risk assessment.
Validation strengthens confidence in the evidence and its implementation, but it does not certify that every future AI response will be correct, complete, unbiased, current, or suitable for professional reliance. Every AI-assisted legal output must still be independently verified against authoritative legal sources.
What Lawluate Does
- Provides evidence-informed AI model recommendations for a selected legal domain, task, and priority.
- Shows the complete recommendation calculation, evidence status, and heuristic adjustments.
- Requires a structured eight-question Duty of Inquiry Assessment before risk calculation.
- Generates Mandatory Governance Actions for every unmet verification requirement.
- Calculates a Governance Risk Score from domain, model, authority recency, and verification completeness.
- Produces a professional PDF audit report and maintains a local assessment history.
What Lawluate Does NOT Do
- Determine whether an AI response is legally correct.
- Automatically verify legal authorities, precedents, or citations.
- Automatically verify factual representations or detect Silent Failure.
- Diagnose bias, epistemic injustice, or Type II Logic Failure automatically.
- Guarantee the quality of any future AI output.
- Endorse or recommend specific AI products or vendors.
- Replace professional legal judgment or transmit any data externally.
Limitations
Lawluate is a governance, recommendation, and audit-support system. It does not automatically verify legal correctness, inspect authorities, diagnose bias, detect Silent Failure, identify Type II Logic Failure, or replace professional legal judgment. Its evidence-informed recommendations support decision-making but do not guarantee the quality of a future AI output.