Check Your Biases

One of the goals of our interviewer training is to minimize bias and maximize hiring accuracy. It can be helpful to think in terms of the following categories:

1. Why might I incorrectly like a candidate more than I should?

2. Why might I incorrectly dislike a candidate more than I should?

3. What can I do about it?

Biases Favoring a Candidate

1. Similarity Attraction: A natural tendency to favor candidates who share our interests, background, or even alma mater, leading to a "like me" bias. This can happen when there is too much small talk about common interests—either before or during the interview.

2. Halo Effect: An impressive characteristic (like a prestigious school or company) can overshadow a candidate’s actual competencies. Don’t let the resume bias your interview or your judgment.

3. Confirmation Bias: Seeking information that confirms our initial positive impression, ignoring red flags. Don’t let their answer to question one effect how you conduct the rest of the interview.

4. Affective Heuristics: Allowing a candidate's charm or charisma to influence the perception of their professional capabilities. Be wary of being “impressed” by someone’s interview “performance.”

5. Overconfidence in Intuition: Trusting a gut feeling over objective data, especially after a seemingly 'good' interview. Force yourself to give ratings for individual categories, backed by data, rather than an overall score.

Biases Against a Candidate

1. Contrast Effect: Comparing a candidate to a strong predecessor or another impressive candidate, rather than against the role’s requirements. Score only against a well-calibrated Target, not against other recent candidates.

2. Stereotyping: Assuming a candidate can or cannot do certain tasks based on age, gender, ethnicity, or other demographic factors. Ensure that you minimize qualifications and dig into stories to get real data that you can calibrate.

3. Negative Emphasis: Overvaluing a single piece of negative information or a minor mistake during the interview. Again, calibrate. For mistakes—how recent, relevant, and big was the mistake? Get consistent in how you evaluate these.

4. Introvert Bias: Being swayed by a candidate's nervousness or lack of eye contact, which may not relate to job performance. Many people hire smooth-talking extraverts over introverts in roles where the extraversion is not particularly relevant. Yet another reason why the interview must be a data gathering expedition and not a performance.

5. Recency Effect: The most recent information about a candidate disproportionately affects the decision, whether positive or negative. Force yourself to review your as-close-to-verbatim-as-possible notes after the interview is over. Don’t simply trust your memory of the interview.

Checking Your Bias: Other Tactics

1. Structured Interviews: Use a consistent set of questions for every candidate to ensure fairness and focus on role-relevant criteria.

2. Blind Resume Reviews: Evaluate resumes without names or other demographic information to prevent unconscious bias.

3. Diverse Interview Panels: Include interviewers from various backgrounds to balance perspectives and mitigate individual biases.

4. Targets: Rate candidates on specific competencies and OKRs after interviews to maintain objectivity.

5. Data-Driven Debriefs: Hold structured debrief sessions where interviewers must provide evidence for their assessments. Hold your teammates accountable for this.

6. Training and Awareness: Provide interviewers with training on potential biases and their impact on hiring decisions.

7. Candidate Feedback Loop: After a candidate is hired, compare their interview assessments with actual performance to identify any consistent biases in evaluations. This “kaizen” approach applies for broadly, but can also be directed at biases—what type of mistakes do you see your interviewing team make?

8. Counterfactual Reasoning: Actively argue the opposite position of your initial impression to challenge your biases. If you can’t articulate at least one risk factor in hiring the candidate, you’re likely suffering from the halo effect (and/or you simply haven’t dug deeply enough).

Everyone is biased. Step one is to admit this, and to examine how it may currently affect your role definitions, your interviewing style, and the way you evaluate candidates. The next step is to start putting a data-driven interview process in place that can channel the wisdom of your collective team while minimizing idiosyncratic biases. That’s how you can reliably grow your talent pool in a high-performing and diverse manner.