Affinity bias is our tendency to like people that are similar to ourselves. This is not a result of a conscious thought process; rather, it is an unconscious bias that happens automatically.
Affinity bias is a type of implicit bias. One of the first researchers to studyaffinity bias were Greenwald and Banaji (1995) as part of their description of implicit cognition.
They defined implicit cognition as when “…traces of past experience affect some performance, even though the influential earlier experience is not remembered in the usual sense—that is, it is unavailable to self-report or introspection” (p. 4-5).
Affinity Bias Examples
- Fraternity Bros: When an employer is interviewing applicants and favors those that were in his same fraternity.
- Sports Pals: Two people instantly “click” when they discover that they both played field-hockey in college
- Hitting it off on a First Date: Going on a first date and realizing that you both like the same types of classic movies and obscure music
- Shared Values: Joining a vegetarian club and feeling a strong bond with other members right away
- Shared Religion: Being naturally attracted to people that share your religious beliefs.
- Shared Professional Interests: Going to an academic conference and congregating with researchers doing work in your area of specialty.
- Shared Personal Interests: Realizing that your fiancé’s father also likes to bake bread.
- Gamer Community: Preferring to hang-out with gamers that like to the play the same games as you.
- Ethnic Bias: A teacher giving higher scores to students that have similar ethnic background to themselves.
- Class Solidarity: Different groups of a social class that are disadvantaged or have less power in society may have stronger bonds with each other.
Detailed Examples
1. Jury Decisions and Characteristics of Defendants
Justice is blind. That means that judges and jurors are supposed to only weigh the facts of the case when making their verdicts. But sometimes affinity bias rears its ugly head!
Being objective is the goal, but it might be a little naïve to believe that always happens.Jurors may be unconsciously inclined to favor defendants that are like themselves demographically.
For example, jurors of a particular ethnic identify may process information differently if the defendant is from the same background. This can also be true for a wide range of other attributes such as educational background, geography, or political affiliation.
In the words of Elek and Hannaford-Agor (2015), “Findings in the scientific research literature demonstrate how implicit bias can operate to distort a person’s interpretations of the evidence in a case” (p. 117).
Distortion is a double-edged sword. Not only can the effects of affinity bias work in favor of a defendant, it can also operate in reverse when there is a lack of similarity between defendant and juror.
Related Article: 15 Blind Spot Bias Examples
2. Cross-Cultural Conflicts
These days people travel all over the world for work. The era of globalization means it is easy for people to find work in a foreign country, and make a pretty good living. But when we travel, we often experience cultural bias an in-group biases.
Of course, nothing is perfect. Living in a foreign country also means being the “odd-man out” sometimes. People naturally prefer to hang-out with people that are similar to themselves. This is especially true when living in a foreign country because of so many cultural differences, language being just one.
Unfortunately, what some foreigners discover is that when they encounter a conflict with a local, things don’t always go their way. Locals may have an unconscious tendency to side with another local.
This might not be intentional, but it still results in the foreigner feeling a bit slighted. Even when the facts of a situation may be clear, people process information differently when they see someone similar to themselves in a disagreement with someone not similar to themselves.
3. The Job Interview
The job interview is a crucial step in the hiring process. One’s performance during the interview can make-or-break a person’s chances of landing the job.
Unfortunately, even having a stellar performance cannot always overcome the power of affinity bias.
One applicant might give all the right responses to the interviewer’s queries, has a great educational background and extensive experience in similar jobs.
But then, the next applicant enters. This applicant went to a lower-tier school and is relatively inexperienced. However, as it turns out, the applicant and the HR head went to the same university, were members of the same sorority, and actually go to the same church every Sunday.
Wow! That’s a degree of similarity that even the most professional HR head will have trouble ignoring. Better luck next time to applicant #1.
4. Political Candidates
Voting for people that are similar to ourselves may be one of the best examples of affinity bias.
There can be a lot of candidates running for office, and they may represent a wide range of demographics.
However, according to the affinity bias, candidates that look like us, talk like us, and were raised like us, will get our vote nine-times out of ten.
When we watch that candidate give a speech or participate in a debate, everything they say and do will be filtered through the lens of the affinity bias. Even when they make a mistake or perform poorly, we might not even notice. It’s as if that instance of failure never even occurred.
The affinity bias has a strong effect on how we interpret other people’s actions.
5. Bank Loans and AI
Asking the bank for a small business loan, mortgage or auto loan is no pleasant endeavor. There is a lot of paperwork and a lot at risk.Although loan officers try to do their best to be fair, the power of the affinity bias can still persist.
Because it is unconscious and automatic, loan officers will not even be aware of their bias.
This is where AI comes into play. Many in the FinTech sector believe that delegating loan decisions to AI algorithms can reduce the effects of biases such as affinity bias.
As stated by Bartlett et al. (2019) “Algorithmic decision-making can reduce face-to-face discrimination in markets prone toimplicit and explicit biases” (p. 2).
An in-depth analysis of AI loan algorithms revealed that “…FinTech algorithms discriminate 40% less than face-to-face lenders” (p. 6).
Conclusion
Affinity bias is a pervasive kind of implicit bias that happens even though we may not want it to. It is unconscious, automatic, and not deliberative.
We are just naturally attracted to people that are similar to ourselves. That similarity can involve attitudes and beliefs, educational backgrounds and social affiliations, or demographic profiles.
It occurs in a wide range of situations, from meeting unfamiliar people to interviewing applicants for an employment position.
Unfortunately, when it comes to legal verdicts by jurors or voting for government offices, affinity bias can have serious consequences. Jurors may find it hard to believe that a juror so similar to themselves is guilty.Voters may be attracted to a candidate that is similar to themselves regardless of their qualifications.
References
Bartlett, R., Morse, A., Stanton, R., & Wallace, N. (2019, November). Consumer-Lending Discrimination in the FinTech Era. Paper published by the University of California, Berkeley. Retrieved from: https://faculty.haas.berkeley.edu/morse/research/papers/discrim.pdf
Elek, J. K. & Hannaford-Agor, P. (2015). Implicit bias and the American juror. Court Review: The Journal of the American Judges Association, 51(3), 116-121. Retrieved from: https://www.ncsc-jurystudies.org/__data/assets/pdf_file/0017/9332/elek-hannaford-agor-2015-implicit-bias-and-the-american-juror.pdf
Ennis, D., & Cook, T. (2019, August 16). Bias from AI lending models raises questions of culpability, regulation. Banking Dive. https://www.bankingdive.com/news/artificial-intelligence-lending-bias-model-regulation-liability/561085/
Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: attitudes, self-esteem, and stereotypes. Psychological Review, 102(1), 4-27.
Malinen, S., & Johnston, L.(2013).Workplace ageism: Discovering hidden bias. Experimental Aging Research,39(4),445-465. https://doi.org/10.1080/0361073X.2013.808111
Rassin, E., Eerland, A., & Kuijpers, I. (2010). Let’s find the evidence: An analogue study of confirmation bias in criminal investigations. Journal of Investigative Psychology and Offender Profiling, 7, 231-246. https://doi.org/10.1002/jip.126
Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.
This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.