📌Notes on Bias (for Interviewers)

It's easy for bias to creep in when we're interviewing and evaluating job candidates. Awareness of bias can help you avoid it in your feedback. Some common biases are:

• Similarity bias.We tend to prefer candidates with similar backgrounds, experiences, or characteristics to ourselves.

• Halo / horns bias.We often give too much weight to a single example or incident from an interview, allowing it to strongly influence our opinion about a candidate.

• Confirmation bias.We sometimes form opinions about a candidate before we meet them based on their resume, work samples, etc., and look for evidence that confirms our opinions when we're interviewing the candidate.

• Performance bias.This type of bias occurs when people who are part of dominant groups, such as whites or men, are judged by their expected potential, while those who are part of less dominant groups, such as people of color or women, are judged by proven accomplishments.

• Performance attribution bias.When it comes to decision-making, unconscious biases cause some people to be perceived as "naturally talented," while others are presumed to have "gotten lucky." People on the receiving end of these biases are less likely to receive credit for their ideas, are interrupted more often during team interactions and have less influence on teams.

• Competence / likability tradeoff bias.Research shows that success and likeability are positively correlated for men and negatively correlated for women. Women are expected to be nurturing and caretaking, while men are expected to be assertive and action-oriented. Having to produce results and be liked makes it harder for women to get hired and promoted, negotiate on their own behalf, and exhibit leadership.

The best way to prevent unconscious bias from affecting decision-making is to develop a standard set of role-relevant behavioral interview questions and a framework for evaluating responses to those questions. Structured, behavioral interviews remain the best approach for mitigating the influence of bias in the decision making process.