SHORTS: Frameworks for auditing algorithmic bias in program evaluations

Frameworks for auditing algorithmic bias ensure systematic checks in program evaluations, where AI might skew impact assessments or beneficiary targeting. These frameworks adapt clinical and general AI auditing practices to M&E by emphasizing stakeholder input, fairness metrics, and iterative monitoring.pmc.ncbi.nlm.nih+1

Five-Step Audit Framework

This stakeholder-driven approach starts with engaging M&E teams, donors, and beneficiaries to define audit goals, risk thresholds, and evaluation scenarios. It proceeds to model calibration using program-specific data, vignette-based testing for bias (e.g., disparate outcomes across demographics), result interpretation against human benchmarks, and ongoing drift monitoring. Automation via scripts enhances reproducibility for repeated M&E cycles.[pmc.ncbi.nlm.nih]​

Assurance Audit Framework

Designed for regulatory compliance, it scopes the AI system’s technical and governance components, verifying bias testing like disparate impact analysis alongside risk assessments and accountability structures. In program evaluations, auditors check if governance mitigates biases in predictive models, providing evidence-based opinions on criterion fulfillment. Criteria cover disparate impact, governance duties, and bias risk identification.[facctconference]​

Seven-Step Bias Detection Process

Practical for M&E practitioners, this involves sequential checks: data representation gaps, model feature review, fairness metric computation (e.g., 80% rule for group outcomes), statistical tests, intersectional analysis, real-world impact simulation, and reporting with fixes. Visual tools like confusion matrices highlight disparities in evaluation predictions across regions or genders.[optiblack]​

Key Auditing Practices

Regular third-party audits review inputs and outputs, using tools like bias impact statements to assess purpose and production data. In M&E, this integrates with logframe validation, prioritizing human oversight for contextual nuances. Canadian Algorithmic Impact Assessments add risk questionnaires for mitigation planning.brookings+1