Law firms should verify AI-generated time entries by comparing each field against reliable source evidence and actual lawyer knowledge.
An entry can look polished and still be wrong.
For example, AI may produce a professional narrative while:
Selecting the wrong matter
Overstating duration
Choosing the wrong billing status
Suggesting an incorrect task code
Combining unrelated work
Including unsupported details
Accuracy therefore needs to be tested field by field, not as one vague percentage.
What Does “Accurate” Mean?
A legal time entry can include:
Entry dimension | Fields |
Identity | Date; Timekeeper; Client; Matter |
Time and status | Duration; Billing status; Review status |
Billing content | Narrative; Task code; Activity code |
Each field can have a different accuracy rate.
A product may be strong at matter matching and weaker at duration estimation.
That distinction matters.
Verify Against Source Evidence
The reviewer should be able to compare the draft entry with evidence such as:
Evidence group | Sources |
Scheduled and communication activity | Calendar event; Email; Call record |
Documents and matter context | Document activity; Matter workspace; Prior approved entry |
Digital activity and time records | Browser activity; Timer; User note |
The system should not require blind trust in the generated result.
Source visibility makes correction faster and improves accountability.
Matter Accuracy
Test:
One client with several matters
Several clients with similar names
Internal matters
Closed matters
New matters
Shared participants
Documents moved between workspaces
Measure:
Matter correction rate = Entries with corrected matter ÷ Entries reviewed
A high correction rate can indicate poor source data, weak matter mapping, or insufficient context.
Duration Accuracy
Duration is difficult because digital activity is not identical to focused work.
Test:
Duration test | Scenarios |
Long and short work | Long drafting sessions; Short emails |
Interruptions and overlaps | Interrupted work; Overlapping records; Idle applications |
Meetings and timers | Meetings; Timer activity |
Offline work | Offline work |
Measure:
Average duration change
Percentage of entries changed
Overlap corrections
Timer-overrun corrections
Do not treat a document's open time as automatic proof of billable duration.
Narrative Accuracy
Evaluate whether the narrative:
Narrative quality area | Checks |
Factual support | Describes actual work; Uses supported facts; Identifies the purpose; Avoids invented legal conclusions |
Confidentiality and client rules | Avoids excessive confidential detail; Follows client language rules |
Clarity and billing format | Avoids vague phrases; Avoids unnecessary block billing |
Possible measures include:
Narrative edit rate
Major rewrite rate
Unsupported-detail rate
Client-guideline exception rate
A grammatically strong narrative can still be inaccurate.
Billing Status and Code Accuracy
Test whether AI correctly suggests:
Billable
Non-billable
No charge
Flat-fee/internal status
Task code
Activity code
Matter phase
Measure:
Billing-status correction rate
Task-code correction rate
Activity-code correction rate
Client-specific code sets should be tested separately.
Build a Ground-Truth Test Set
Before deployment, create a sample of known workdays.
Include:
Test-set dimension | Coverage |
People and practices | Several lawyers; Several practice areas |
Task length and matter variety | Short tasks; Long tasks; Several matters; Flat-fee work |
Activity boundaries | Internal activity; Personal activity; Offline work |
Data-quality challenges | Client-specific guidelines; Overlaps; Duplicate signals |
Have knowledgeable reviewers establish the expected result.
Then compare AI suggestions against that baseline.
NIST guidance for generative AI recommends evaluating accuracy, quality, reliability, and authenticity against known ground truth where appropriate.
Separate Automation Accuracy from Review Accuracy
There are at least two questions:
How accurate was the AI suggestion?
How accurate was the final approved entry?
A system can have imperfect suggestions and still produce strong final data when the review process is effective.
Conversely, a strong model can still produce bad billing data if users approve entries without reading them.
Measure both layers.
Sampling and Ongoing Audit
After launch, periodically sample:
Audit sample | Entries to include |
Baseline sampling | Random entries |
Risk and confidence | High-value matters; Low-confidence entries; Entries approved without edits; Entries with long duration |
Complex activity | Overlapping activity; Client-rule exceptions |
Change and adoption | New users; New practice areas |
The audit should record:
Error
Cause
Severity
Corrective action
Owner
Follow-up
False Positives and False Negatives
For validation alerts:
False positive
The system flags an entry that is actually correct.
False negative
The system fails to flag an incorrect entry.
Too many false positives create alert fatigue.
False negatives allow errors to pass.
Measure both when evaluating rule and AI performance.
Human Approval Is a Control
ABA guidance on AI and legal billing emphasizes that lawyers remain responsible for supervising AI billing outputs.
A useful review interface should allow the lawyer to:
See source evidence
Change matter
Change duration
Change billing status
Rewrite narrative
Correct codes
Split or merge entries
Exclude activity
Approve explicitly
Human review should be meaningful, not a checkbox.
Monitor Model and Configuration Changes
Accuracy can change after:
Model update
Prompt change
Matter-mapping change
Integration update
New code set
Client guideline update
New practice area
New data source
The firm should retest important workflows after material changes.
This is part of AI governance.
Accuracy Dashboard
Track:
Dashboard area | Metrics |
Matter and duration | Matter acceptance rate; Matter correction rate; Duration correction rate |
Billing content | Billing-status correction rate; Narrative edit rate; Task-code correction rate; Activity-code correction rate |
Detection quality | Duplicate detection rate; False-positive rate |
Review and downstream quality | Entries approved without change; Review time; Prebill correction rate |
Avoid reporting only one overall accuracy score.
Procurement Questions
Ask vendors:
Procurement topic | Questions |
Explainability and control | Can users see source evidence?; Can confidence be shown?; Can users correct every field? |
Measurement and audit | Are field-level metrics available?; Can the firm export audit data?; Can pilot data be benchmarked? |
Change and AI governance | Are model changes documented?; Can AI suggestions be disabled?; Is firm data used for shared training? |
Billing rules and data quality | How are client rules applied?; How are duplicates handled?; How are overlaps handled? |
How MIRA Supports Verification
MIRA is designed around reviewable time capture rather than invisible automatic billing.
MIRA materials describe captured time presented inside Microsoft Teams, where users can edit descriptions, merge entries, exclude activity, save drafts, and release reviewed time. MIRA also supports AI-assisted task/activity code and billing-description suggestions.
That makes the human review step part of the product workflow.
Related Resources
Authoritative References
This guide is for educational purposes only. AI, privacy, monitoring, billing, employment, and professional-conduct requirements vary by jurisdiction and firm. Firms should conduct appropriate legal, ethics, privacy, security, and operational review before deployment.
Frequently Asked Questions
What is the most important AI timekeeping accuracy metric?
There is no single metric. Matter, duration, billing status, narrative, and billing-code accuracy should be measured separately.
Should firms trust entries that need no edits?
They should still use sampling and audit because users can approve an incorrect entry without changing it.
How large should a pilot test set be?
It should be large and varied enough to represent the firm's real work patterns, practice areas, matter types, short tasks, long tasks, and exceptions.
Can AI accuracy decline after launch?
Yes. Model, integration, rule, matter-data, and client-guideline changes can affect performance.
How does MIRA support verification?
MIRA presents captured time and AI-assisted suggestions for user review before approved entries are released toward supported finance systems.
