Familiar old ways of hiring are failing us more obviously now. Employers want a better approach to hiring that fairly assesses candidates during video interviews, reduces bias throughout the hiring process and builds more diverse and inclusive teams. Employers are frustrated, recognizing that differential treatment by race (and other factors) is still commonplace in hiring. But even with manager training and the best of intentions, outdated hiring practices often result in hiring people who are demographically similar to their hiring manager.

In this session you will learn: what hiring managers usually get wrong when they assess candidates during interviews, how and where your familiar hiring practices introduce bias into the hiring decision, the common mental errors that lead us all to reach incorrect conclusions about candidates.

In the second webinar in this series, we'll put this all into practice, covering:

• How to reduce bias when selecting resumes
• The necessary elements of creating a diverse slate of candidates
• How to conduct a “Blind Interview”
• The key elements of Evidence-Based Interviewing
• What factors best predict success on the job
• How to assess cultural fit (without perpetuating bias)

You can see the next session here.

Source material:

Cognitive Bias Source Material. A cognitive bias is mental shortcut that can lead to poor decision-making. It was first proposed by Amos Tversky and Daniel Kahneman in a 1974 article in Science. Since then, researchers have identified many other types of cognitive bias. We recommend Thinking Fast and Slow by Daniel Kahneman. An outstanding book on topic of choice architecture is Nudge by Richard Thaler and Cass Sunstein.

Here is a list of the biases we referenced in the presentation, and sources for more information:

• Blindspot Bias. In one survey of 661 people, only one person reported being more biased than the average person. Source: https://www.cmu.edu/news/stories/archives/2015/june/bias-blind-spot.html
  
  
• Confirmation Bias. people are biased toward confirming their existing beliefs.
  
  
• Dunning-Kruger Effect: The cognitive error that causes people with low competence to overestimate their ability, and people with higher competence to underestimate their ability.
  
  
• Fundamental Attribution Error: When we assume that the actions of individuals are caused by who they are rather than the circumstances around them: https://www.marketwatch.com/story/performance-reviews-dont-work-the-way-we-think-they-do-2016-06-23
  
  
• Halo Effect the tendency for positive impressions of a person in one area to influence one's opinion in other areas. For example in an interview, when a candidate is attractive, well groomed, and properly attired, one might assume that person is a good person.
  
  
• Looking Glass Merit. Evaluators use themselves as models of merit not only when assessing soft skills and intangibles but also when estimating hard skills. Source: https://journals.sagepub.com/doi/full/10.1177/0003122412463213?mod=article_inline

And here is a list of other common cognitive biases: https://en.wikipedia.org/wiki/List_of_cognitive_biases

Additional source material:

“Employers sought candidates who were not only competent but also culturally similar to themselves in terms of leisure pursuits, experiences, and self-presentation styles.” Source: Hiring as Cultural Matching American Sociological Review: https://journals.sagepub.com/doi/full/10.1177/0003122412463213

There are more CEOs of large U.S. companies who are named David (4.5%) than there are CEOs who are women (4.1%) — and David isn’t even the most common first name among CEOs. Source: https://hbr.org/2016/04/if-theres-only-one-woman-in-your-candidate-pool-theres-statistically-no-chance-shell-be-hired

McKinsey & Company reported that companies in the top quartile for gender diversity are 15 percent more likely to have financial returns above their industry averages, and companies in the top quartile for racial and ethnic diversity are 35 percent more likely to outperform their peers. Source: https://www.mckinsey.com/business-functions/organization/our-insights/why-diversity-matters

Boston Consulting Group found that even small increases in diversity led to more innovation - more revenue being generated from innovative new products and services. Source: https://www.bcg.com/en-us/publications/2018/how-diverse-leadership-teams-boost-innovation.aspx

Columbia professor Dr. Katherine Phillips “Diversity jolts us into cognitive action in ways that homogeneity simply does not.” Source: https://www.scientificamerican.com/article/how-diversity-makes-us-smarter/ Also see her presentation of this material: https://www.youtube.com/watch?v=lHStHPQUzkE

Force Multiplier. Source: Lt. Gen. Jay Silveria, the Air Force Academy superintendent, says diversity, “…makes us better, stronger and more effective as a fighting force.” It is, “…the application of our collective intelligence -- our uniqueness coming together to fulfill our duty … Diversity is a force multiplier.” Source: https://www.airforcetimes.com/news/your-air-force/2018/02/14/lt-gen-jay-silveria-diversity-is-a-force-multiplier/

When a top insurance company changed how they evaluated candidates for “culture fit,” they ended up offering jobs to 46% more minority candidates than before. Source: https://hbr.org/2019/11/how-the-best-bosses-interrupt-bias-on-their-teams?

Arguably, the greatest failure of organizational psychology has been the inability to convince employers to use decision aids to reduce error in predicting employee performance. Source: https://blogg.hrsverige.nu/wp-content/uploads/2010/04/Stubborn1.pdf

Wharton Professor Peter Cappelli demonstrated that in their performance reviews, employees tend to get credit or blame for circumstances – factors beyond their own performance. Source: https://hbr.org/2016/07/the-common-myths-about-performance-reviews-debunked

In another article on the same study, Wharton Professor Peter Cappelli noted, “The best predictor of a good rating is your demographic similarity to your boss.” And, “The more different you are in terms of ethnicity or age or sex, the less well you’re going to do.” Source: https://www.marketwatch.com/story/performance-reviews-dont-work-the-way-we-think-they-do-2016-06-23

Marcus Buckingham concluded that, “…virtually all of our people data is fatally flawed.” He added, “…neither you nor your peers are reliable raters of anyone.” And, “…on average, 61% of my rating of you is a reflection of me.” Buckingham coined this, the “Idiosyncratic Rater Effect,” primarily due to a “Shifting Baseline” in how we rate others. Source: https://hbr.org/2015/02/most-hr-data-is-bad-data

Peer reviewed research on best practices in hiring:  https://bobsutton.typepad.com/my_weblog/2009/10/selecting-talent-the-upshot-from-85-years-of-research.html

How to assess cultural fit without perpetuating bias: https://blog.staffingadvisors.com/how-to-assess-cultural-fit-without-perpetuating-bias

Google’s re:Work website also has a collection of practices, research, and ideas to help you follow evidence-based practices: https://rework.withgoogle.com/subjects/hiring/