Selected work / 01

AI implementation · World Bank Group

Making AI-assisted legal research reviewable by design.

I designed and built a controlled pilot that decomposes cross-country research into source discovery, evidence checks, structured analysis, and analyst approval.

RoleData & AI Research Intern, DECIG
TeamWomen, Business and the Law
PeriodMay–August 2026
StatusControlled pilot completed
Operating problem

Research requires both faster source discovery and accountable legal judgment.

System response

Separate stages, evidence states, routing rules, and a defined review queue.

What the evidence supports

A functioning pilot and inspectable workflow—not measured adoption or productivity impact.

Control model

Automation advances evidence. Analysts retain the decision.

This is a public reconstruction of the top-level design. Internal prompts, code, data, and detailed test results are omitted.

Reconstructed architecture Evidence and approval boundaries
01Automation laneEvidence preparation
Stage 1Source discoveryCandidate instruments
Eligibility gate
Stage 2Evidence verificationScope · date · support
Support gate
Stage 3Structured analysisEligible pool only
Structured evidence + review state
02Accountability laneSubstantive decision
Review queueInspect evidenceTrace sources and status
Analyst authorityApprove, revise, or holdNo automatic final coding

01 / Problem and response

The design problem was not simply finding documents.

Cross-country legal research requires analysts to locate relevant instruments, determine whether a source covers the right jurisdiction and period, interpret provisions against a structured question, and preserve enough evidence for another researcher to review the judgment.

A single-pass assistant would collapse those activities into one opaque response. I translated the existing research process into a staged system where automation could support selected steps without inheriting authority over final coding decisions.

My contribution

Workflow architecture, orchestration and agent logic, evidence and confidence states, deterministic routing, analyst review outputs, and layered validation.

02 / Decision log

Three choices shaped the system.

Each choice trades some apparent simplicity for a workflow that can be inspected, tested, and handed back to a domain expert.

01

Separate retrieval from substantive judgment.

Why

Finding a plausible source is not the same as establishing that it supports a legal conclusion. Discovery proposes candidates; later stages evaluate eligibility and support.

Tradeoff

More orchestration and latency in exchange for clearer error boundaries and reviewable evidence.

02

Route evidence through explicit states.

Why

Structured status fields and deterministic rules make it clear what may advance, what needs another pass, and what must be held for review.

Tradeoff

More schema and control logic in exchange for predictable handoffs between probabilistic components.

03

Make human approval a system state.

Why

The analyst receives traceable evidence and a defined decision point. Human review is part of the operating flow, not a disclaimer after generation.

Tradeoff

The pilot supports rather than replaces expert work; the final throughput depends on review capacity.

03 / Validation

System behavior and substantive quality were treated as different questions.

Software checks covered deterministic behavior, state transitions, expected schemas, routing rules, and edge cases. Pilot outputs were then compared with manually coded reference material and prepared for analyst review.

Passing software checks establishes that the workflow behaves as specified. It does not, by itself, establish that a legal conclusion is correct.

Established by the work
  • Functioning multi-stage controlled pilot
  • Structured evidence and review artifacts
  • Documented operating logic and handover package
Not yet established
  • Organization-wide or recurring adoption
  • Measured productivity or cost impact
  • Fully automated legal accuracy

Time-saving estimates and planned integration were planning assumptions, so they are excluded from the measured results presented here.

04 / Public boundary

What can be shown publicly.

The case describes the problem, my role, the top-level control model, and the reasoning behind key design choices.

Internal code, prompts, datasets, reports, screenshots, and detailed test results remain outside the public portfolio.