When AI Recommendations Conflict With User Evidence: How Research Leaders Decide

By Philip Burgess | UX Research Leader

Artificial intelligence has become a powerful tool in research, offering data-driven recommendations that can accelerate discovery and improve decision-making. Yet, what happens when AI suggestions clash with evidence or insights gathered by researchers themselves? This tension between machine-generated advice and human judgment is a challenge many research leaders face today. I want to share how I and others in the field navigate these conflicts and make decisions that balance AI input with user evidence.

Researcher reviewing AI-generated data alongside experimental results

Understanding the Conflict Between AI and User Evidence

AI systems rely on patterns in large datasets to generate recommendations. These can range from suggesting new hypotheses to prioritizing experiments or identifying trends. However, AI models have limitations. They may not capture nuances in experimental design, contextual factors, or recent findings that have not yet been included in training data.

On the other hand, researchers bring deep domain knowledge, intuition, and firsthand experience. They often gather evidence through experiments, observations, or pilot studies that may contradict AI outputs. When these two sources disagree, the decision is not straightforward.

I recall a project where our AI tool recommended focusing on a particular gene for cancer therapy. Our lab’s recent experiments, however, showed inconsistent results with that gene’s involvement. The AI’s suggestion was based on a vast dataset, but our evidence pointed elsewhere. This situation forced us to carefully weigh both sides before moving forward.

How Research Leaders Approach These Situations

1. Evaluate the Quality and Scope of Evidence

The first step is to assess the reliability of both AI recommendations and user evidence. AI outputs depend on the quality of input data and the model’s design. If the AI was trained on outdated or biased data, its suggestions might be less trustworthy.

Similarly, user evidence must be scrutinized for experimental rigor, sample size, and reproducibility. Anecdotal or preliminary findings should not outweigh robust AI insights without further validation.

In my experience, combining these assessments helps clarify which source holds more weight in the specific context.

2. Engage in Collaborative Discussion

Decisions are rarely made in isolation. Bringing together AI specialists, domain experts, and frontline researchers fosters a shared understanding. These conversations reveal assumptions behind AI models and the context behind user evidence.

For example, in the gene therapy case, our team held a series of meetings where bioinformaticians explained the AI’s data sources and algorithms. Meanwhile, lab scientists presented their experimental protocols and results. This dialogue helped identify gaps in both approaches and guided a more informed decision.

3. Design Targeted Experiments to Test Conflicts

When AI and user evidence diverge, designing new experiments to specifically test the conflicting points can provide clarity. This approach turns uncertainty into an opportunity for discovery.

We decided to run additional trials focusing on the gene in question, using different cell lines and conditions. These experiments helped us understand the gene’s role better and eventually reconcile some of the discrepancies.

4. Maintain Flexibility and Update Decisions

Research is dynamic. As new data emerges, both AI models and user evidence evolve. Research leaders must remain open to revisiting decisions and updating strategies.

In practice, this means setting checkpoints to review outcomes and adjust plans. It also involves refining AI models with fresh data and incorporating user feedback to improve accuracy.

5. Document the Decision-Making Process

Transparency is key. Documenting how conflicts were resolved, what evidence was considered, and why certain choices were made helps build trust within the team and with external stakeholders.

Our team kept detailed records of discussions, experiments, and rationale. This documentation proved valuable when publishing results and explaining our approach to collaborators.

Whiteboard showing a flowchart of AI and user evidence evaluation in research decisions

Practical Tips for Research Leaders Facing AI-User Evidence Conflicts

• Prioritize transparency: Make sure everyone understands how AI recommendations are generated and what user evidence exists.

• Encourage open communication: Create forums where team members can voice doubts and share insights without judgment.

• Use AI as a guide, not a rule: Treat AI suggestions as one input among many, not the final word.

• Invest in training: Help researchers understand AI capabilities and limitations to better interpret recommendations.

• Plan for iterative testing: Build cycles of testing and feedback into research workflows to resolve conflicts quickly.