AI is only as fair as the humans and data that train it. In document review, both Continuous Active Learning (CAL) systems and Generative AI workflows depend heavily on reviewer input through coded decisions.
When reviewers bring bias (conscious or unconscious) into their coding, it gets amplified by the models and can escalate into systemic patterns, creating a new challenge for document review. Now, we not only need to focus on building smarter models, we also need to foster more consistent, less biased human reviewers and design workflows that validate fairness in decisions.
In practice, this means that even sophisticated AI tools are only as trustworthy as the reviewers guiding them. A single reviewer’s assumptions about relevance or privilege can waterfall through the entire training process. If unchecked, these biases can affect which documents get prioritized for review, how privilege logs are built, or even how responsive documents are clustered.
In litigation or regulatory investigations, this can lead to reputational risk, discovery disputes, or inadvertent disclosure. The conversation around AI fairness in review isn’t theoretical, it needs to be measurable and defensible.
How Reviewer Bias Amplifies AI Bias
In traditional review, bias might mean one reviewer tags Executive emails as “more relevant” by default (authority bias) or overlooks documents written in non-standard English (linguistic bias). In CAL, those coding decisions become training data, directly steering the machine’s predictions.
In GenAI-assisted review, the risks increase. The model can take these inputs, generate skewed summaries or coding rationales, and reinforce that across thousands (or millions) of documents. More simply put, biased reviewers create biased training data which creates biased AI outputs.
Consider a typical scenario where a review team is analyzing internal communications during a corporate investigation. If reviewers unconsciously treat technical staff emails as less relevant than executive communications, the CAL model learns to deprioritize those types of documents. When a GenAI tool later generates summaries or rationales, it mirrors that bias, highlighting executive language and downplaying other perspectives. Over time, that becomes a systemic pattern, not a one-off error.
Bias can also emerge when reviewers make cultural or linguistic assumptions. A phrase that sounds sarcastic in one region may be neutral in another. If those cues aren’t calibrated, the AI learns misaligned signals. This is how subtle human inconsistency becomes amplified into machine-level bias.
Training Reviewers to Reduce Bias
The first line of defense in document review is always people. Reviewer training can move beyond coding instructions to include bias-awareness and consistency techniques. These can include:
- Bias awareness modules: Teach common biases (confirmation, authority, linguistic, cultural) and how they show up in document review. Use case studies or anonymized examples from past reviews to show how small assumptions changed outcomes.
- Consistency drills: Run exercises where reviewers code the same set independently, then discuss discrepancies. Highlight how personal interpretation can creep in. For instance, have teams explain why they marked certain emails privileged or nonresponsive, and then show how differences stem from tone interpretation rather than substance.
- Blind coding exercises: Remove custodian names or organizational context from training sets to reduce status-based assumptions. This is especially useful in internal investigations, where rank or department can influence coding subconsciously.
- Cross-team calibration: Rotate reviewers between projects to normalize coding standards across teams. Different teams often develop their own “micro-cultures” of decision-making; rotation helps identify and correct those drift patterns.
The goal is not to erase subjectivity but to minimize variance and make reviewers more conscious of their own bias. Building this into onboarding and periodic refresher sessions keeps the awareness current and measurable. Firms can even use metrics, like inter-reviewer agreement scores, to show improvement over time.
Now, not all of these strategies can be used all the time, but keeping an eye towards bias will only serve to reduce it.
Training Models with Bias in Mind
For CAL and GenAI workflows, the training process itself must account for bias.
- Balanced training sets: Make sure initial seed sets are diverse across custodians, communication styles, and formats. If the seed set overrepresents a single department or communication channel, the model’s “understanding” of relevance becomes skewed. For example, if all early training data comes from leadership emails, the model might miss relevant technical or operational discussions later.
- Bias-aware prompts in GenAI: For GenAI tools, create prompts that explicitly require the model to explain reasoning and flag uncertainty. This reduces the risk of overconfident but biased answers. For example, instead of “Summarize why this document is relevant,” use “Summarize potential relevance and note any uncertainty or conflicting indicators.” This teaches the model to “contextualize” rather than “assert”.
- Iterative retraining with QC feedback: Retrain models on new data, as well as corrected data where bias or inconsistency was identified. Include feedback loops between reviewers and model trainers so that human error doesn’t become permanent machine memory. Each retraining cycle should document what changed and why, improving transparency and defensibility.
These steps ensure that bias detection becomes part of the review lifecycle, not a one-time calibration.
Building an Ethos of Inclusive AI Review
Bias reduction is not new to AI workflows, and, at its core, it’s still a human-driven problem. Organizations should normalize conversations about fairness, encourage reviewers to surface uncertainty, and make transparency part of the workflow. By training reviewers to recognize their own blind spots, structuring model training carefully, and QC’ing AI outputs, teams can prevent bias from scaling up through CAL and GenAI reviews.
That internal shift starts with leadership. Project managers and review leads should model curiosity rather than certainty, encouraging teams to flag questionable patterns or outcomes. Additionally, including bias audits as part of quality control sends a signal that fairness is a shared responsibility, not an optional ideal.
Teams can also track metrics like reviewer agreement rates, model accuracy across demographics, or the distribution of relevance hits by custodian. These data points can provide early warning signs of systemic bias. When fairness becomes a visible metric, reviewers learn that their precision matters beyond productivity, and they can shape outcomes.
In a legal landscape where GenAI-assisted review is standard, fairness shouldn’t be a compliance box; it should be a measure of credibility. Teams that make bias management part of their everyday review workflow will produce work that’s faster, more defensible, and trustworthy.
Caragh Landry
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Caragh brings over 20 years of eDiscovery and Document Review experience to TCDI. In her role as Chief Legal Process Officer, she oversees workflow creation, service delivery, and development strategy for our processing, hosting, review, production and litigation management applications. Caragh’s expertise in building new platforms aligns closely with TCDI’s strategy to increase innovation and improve workflow. Her diverse operational experience and hands on approach with clients is key to continually improving the TCDI user experience. Learn more about Caragh.
