Reviewers can revise coding decisions in Active Learning projects, and here’s why it matters.

Outline in a nutshell

• Set the scene: review work needs flexibility as new insights appear.

• What Active Learning means in Relativity: reviewers can re-open decisions on already reviewed documents.

• The truth behind the question: why True is the right answer and what that looks like day to day.

• How it actually happens: a clear, practical flow—from labeling to re-labeling with an audit trail.

• Why this flexibility matters: accuracy, collaboration, and healthier project momentum.

• Quick tips for project managers: governance, timing, and workflows that support ongoing refinement.

• Common myths and gentle debunking.

• A few down-to-earth analogies to keep the idea memorable.

• Takeaway: adaptability as a core strength in systematic reviews.

Active Learning in Relativity: a quick map

Let me explain the core idea in plain terms. In Relativity’s Active Learning setup, the system isn’t a one-and-done labeling gauntlet. It’s a living process. After reviewers mark documents, the software learns from those labels and suggests what to review next. Importantly, the people doing the reviewing aren’t locked into their first judgments. They can revisit and adjust decisions on documents that were already reviewed. This isn’t “cheating”; it’s how you tighten accuracy when new context emerges.

True, not True fantasy

Here’s the thing: the notion that decisions can be revisited is central to Active Learning. The correct answer to the question—whether reviewers can change coding decisions on already-reviewed documents—is True. That flexibility is not an afterthought. It’s built into the workflow because the data landscape shifts. You might uncover a nuance you hadn’t considered, or you might see a pattern that changes how you classify a set of documents. The ability to go back and adjust is what keeps the process honest and iterative, not brittle or robotic.

How it plays out in practice

Think of the review as a conversation, not a transaction. A typical loop looks something like this:

• You label a batch of documents. The AI model takes note of your decisions.

• The system surfaces uncertain or high-value documents for the next pass.

• You re-examine some of the earlier decisions in light of new insights or updated criteria.

• You commit changes. The audit trail records who changed what and when.

• The model updates its understanding, and the cycle continues.

A couple of concrete touchpoints help make this smooth:

• Versioning and audits: each change—who did it, what was changed, and why—gets logged. If someone asks, you can retrace the reasoning step by step.

• Transparent annotations: when you adjust a label, you can leave a note about the rationale. That note travels with the document through future rounds, keeping the thread intact.

• Role-based access with sensible guardrails: while reviewers can revisit decisions, project managers can set controls to prevent drift from the established criteria. It’s about balance, not bans.

Why this is a win for outcomes

Flexibility isn’t fiddling around. It’s a disciplined way to improve reliability and confidence in results. When new patterns emerge or when a subset of documents reveals a different angle, being able to adjust helps prevent errors from compounding. It’s also a boon for collaboration. If one reviewer spots a nuance that others missed, the team can align quickly, avoiding duplicated effort and conflicting labels.

From a project-management lens, a few benefits stand out:

• Better accuracy over time: the model and the human reviewers learn from each iteration.

• Stronger defensibility: an auditable path shows how decisions evolved, which is important for governance and quality reviews.

• Less rework in later stages: early corrections save chasing down inconsistencies later on.

Practical tips to keep the process sane

If you’re managing a Relativity-based review with Active Learning, here are quick moves to keep things efficient and trustworthy:

• Set review cadence and decision windows: schedule regular checkpoints where reviewers re-evaluate critical labels. Consistency matters, and timeboxed cycles help maintain momentum.

• Use targeted re-review: don’t chase every single change. Focus on documents with high impact or ones that triggered model uncertainty.

• Document rationale clearly: short notes on why a decision changed help future readers understand the shift and maintain alignment.

• Leverage the audit trail: routinely spot-check changes to confirm they followed the established criteria and didn’t drift into subjective territory.

• Foster a guided collaboration culture: encourage teammates to discuss divergent labels in a shared space. A quick group debrief can save hours down the line.

Debunking a couple of common myths

Myth 1: Only a super user can tweak decisions.

Reality: While roles and permissions matter, the core capability to revise decisions on previously reviewed documents is a designed part of Active Learning workflows. Role-based controls keep things safe, but the fundamental option to adjust exists as part of the process.

Myth 2: Changes must happen only during the first pass.

Reality: The value of revisiting decisions isn’t tied to a single moment. The landscape evolves as more labels come in, models adjust, and new insights emerge. The idea is ongoing refinement, not a sprint with a fixed start and finish.

Analogies that stick (without getting heavy)

• Editing a manuscript: your initial draft is not final. You go back, revise a paragraph after a reader’s feedback, and the later chapters reflect those changes. The end product reads more coherently because the edits are intentional and tracked.

• Software patch cycles: you push a fix, monitor how it behaves in real use, and tweak again if something unexpected pops up. The system learns from those adjustments, becoming more reliable over time.

• A collaborative playlist: at first you add songs you think fit. Others tweak the order, swap a track, or remove a misfit. The final vibe isn’t set in stone on day one; it’s tuned through collaboration.

Relativity and the broader workflow

Relativity’s philosophy around Active Learning is to support a workflow that mirrors how real teams work: iterative, collaborative, and evidence-driven. The goal isn’t to chase a perfect classification in a single pass. It’s to nurture a living set of decisions that improves as more is learned. That’s why the capability to revisit and revise remains a core feature, not a niche option.

A few thoughts on culture and discipline

• Embrace the loop, don’t fear it. The ability to change decisions should feel like a tool that increases trust in the results, not a loophole to sidestep accountability.

• Keep the line of sight between criteria and actions. When a label changes, the justification should map cleanly to the rules or guidelines you’re following.

• Build in periodic truth checks. A quick audit every so often to confirm that decisions align with the known criteria helps keep drift at bay.

Closing the loop: the big takeaway

Here’s the bottom line: in Active Learning environments, reviewers can revisit and adjust their coding decisions on documents that have already been reviewed. This capability is not a loophole; it’s a structured way to respond to new insights, maintain accuracy, and keep collaboration healthy. The system is designed to support ongoing refinement, with a clear trail that makes every adjustment understandable and reversible if needed. In other words, adaptability isn’t a weakness here—it’s a strength that makes the entire review process more trustworthy and effective.

If you’re navigating a Relativity-driven workflow, remember: flexibility paired with good governance beats rigidity every time. Keep communication crisp, keep the audit trail clear, and let the team breathe in the rhythm of continual improvement. The result? More reliable classifications, smoother collaboration, and a workflow that stays aligned with how real data behaves in the wild.