Building Dashboards to Spotlight Leakage and Friction in Hiring

## The $50K Hallucination Imagine this: Your AI model just hallucinated in production, leading to a costly $50K in customer refunds. This scenario is all too common in hiring processes where false accepts can result in hiring candidates who do not meet the necessary qualifications. When your dashboard lacks the right k

ey metrics, you risk not only financial loss but also reputational damage. Engineering leaders must recognize that a single oversight can cascade into larger systemic failures, making it imperative to spot leakage in real-time.

## Why This Matters The stakes are high. False accepts can lead to unqualified hires, while false rejects may eliminate top talent. Both scenarios can impact the overall quality of hires and the company's bottom line. By focusing on leading indicators, you can predict potential drop-offs and fraud attempts, allowing

for timely interventions. This is not just about compliance; it’s about building a resilient hiring process that can adapt to emerging threats.

## How to Implement It 1. **Define Key Metrics:** Start by identifying what constitutes a false accept and false reject in your context. Metrics like Fraud Acceptance Rate (FAR) and Fraud Rejection Rate (FRR) are crucial for understanding your current state. 2. **Set Up Dashboards:** Use tools like Tableau or PowerBI

to create dashboards that visualize these metrics. Ensure they are integrated with your ATS for real-time updates. 3. **Integrate Telemetry:** Leverage telemetry data to track reviewer actions and candidate interactions. This data can reveal patterns that indicate potential fraud or unnecessary friction in the hiring.

process. 4. **Regular Review Cycles:** Establish a cadence for reviewing these metrics. Monthly or quarterly reviews should involve cross-functional teams to discuss findings and adjust policies, ensuring continuous improvement. ## Key Takeaways - Always validate AI outputs against real-world performance to mitigate