1. Data-driven decision making: Are interviewers meticulously recording interviews and completing scorecards? Is scoring done immediately or postponed, allowing bias to creep in? Do they rely on hiring process data or gut instincts for final decisions?

2. Equitable decision-making: Are your interviewers systematically favoring candidates with specific characteristics? I.e., gender, ethnicity, specific types of educational institutes or companies? What are the prevalent biases that prevent them from making more equitable decisions? (also see the “track bias' ' section in focus area 1).

3. Quality of hire per interviewer: Among candidates, interviewers approved and later hired, what percentage remained in the company for over two years or any chosen period? Who among them yielded high manager satisfaction? This metric aids in identifying top-performing "dream team" interviewers for learning and emulation.

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