AI vs Human Bias in Grant Decisions
Can automation reduce inequality? Practical steps to make processes fairer.
AI vs Human Bias in Grant Decisions
AI can reduce some inconsistencies by standardising reviews, but it can also encode bias if not governed well; a combined approach works best.
- Structured criteria and templates reduce subjective drift.
- Blind reviews and conflict logging increase fairness.
- Audited AI outputs add consistency with human oversight.
Where bias creeps in
Bias can appear in data, prompts and unstructured discussion.
- Over‑weighting brand names or writing style.
- Geographic or thematic preferences not in criteria.
- Inconsistent treatment of similar evidence.
Key takeaway: standardise evidence and scoring to narrow the gap.
Controls that help
Use simple safeguards before complex fixes.
- Calibration sessions and example scores.
- Blind or partially blind reviews where feasible.
- Explanations and audit logs for AI‑assisted steps.
Key takeaway: Plinth supports fair, explainable workflows.
Measuring improvement
Track disparities and iterate calmly.
- Compare acceptance rates by theme, size and geography.
- Review edge cases and appeals to refine criteria.
- Publish an annual fairness statement.
Key takeaway: transparency builds trust and drives progress.
FAQs
Can AI eliminate bias?
No. It can reduce variance but still needs human judgement and monitoring.
Should we hide applicant identities?
Where practical, yes; at minimum, focus reviewers on evidence vs criteria.
What if disparities persist?
Revisit eligibility, outreach and support offers; update criteria accordingly.