An applicant may hold all of the necessary required skills, but comes across as conceited or arrogant. The boss is human, and most likely wants to work with someone who will make their life easier and could forge a comfortable rapport with their direct report.
To demonstrate likability, you need to be genuinely interested in other people. Really listen to what the interviewer says, instead of just waiting for your turn to talk. Focus on the interviewer, as if they are the only person who exists. Slightly nod your head when they are speaking, as it shows that you are in agreement and listening intently. Repeat back important phrases that the interview said to both show you are paying attention and ensuring that you are on the same page.
Offer sincere compliments. If an uncomfortable topic arises, such as why you were let go during the pandemic, it's easy to get flustered and frustrated having to relive this tough time. I became too complacent and would have stayed there another 10 years. Confidently speak and be enthusiastic. This means that any employer interested in eliminating handicaps against less attractive people should be able to detect this bias, and evaluate the efficacy of any intervention.
Now the bad news: you are unlikely to achieve this unless you replace human intuition with data — this is where AI can potentially help, if approached responsibly. So how can we tackle the attractiveness bias? First, you can measure attractiveness, which is typically a function of consensual ratings of physical appearance. Imagine you ask 10 people to rate people on physical appearance or attractiveness.
Although attractiveness is not objective, which is why there are always disagreements between people rating the same person, it is also not entirely subjective, so most people will tend to agree on whether someone is more or less attractive, for instance by using a point scale, and not just when they belong to the same culture.
Next, you can correlate this score with a range of success indicators, from interview ratings, to job performance ratings, to promotion or salary data. A pro-attractiveness bias already exists in education, with studies showing that physically attractive students tend to obtain higher grades at university, partly because they are deemed more conscientious and intelligent, even when they are not.
Furthermore, attractiveness already helps students get into universities in the first place, by eliciting more favorable evaluations during college admissions interviews.
In fact, meta-analytic studies suggest that even children are assumed to be smarter, more honest, and driven, when they are deemed more attractive — and children make the same type of inferences when they evaluate more or less attractive adults.
Unsurprisingly, the beauty bias transfers into the workplace, with scientific studies showing that less attractive individuals are more likely to get fired , even though they are also less likely to be hired in the first place.
Studies suggest that good-looking people are more likely to get hired , promoted and elected to public office. But new research published in the Journal of Personality and Social Psychology found that attractive people may not always be better off.
Researchers from the London Business School conducted a study with over participants who were each given a photo of an attractive person and a photo of a less attractive person and asked to theoretically "hire" one for a job. The study found that participants were less likely to choose attractive people for less desirable jobs. Research several colleagues and I conducted recently suggests that companies may be wise to take this approach with customers.
In our first study, we wanted to better understand how consumers respond to attractive service employees. We invited college students to read the same description of being served dinner at a restaurant and then look at an image of a person we described as their waiter.
Participants randomly viewed either a male or female server whose facial features were edited to depict high or low levels of attractiveness, based on prior research defining beauty.
Separately, we used similar objective measures of attractiveness to rate participants on the same scale.
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