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Running Head: REDUCING IMPLICIT ATTRACTIVENESS BIAS

Investigating the Effect of Increased Awareness on Reducing Implicit Attractiveness Bias

Anne Carter Payne

University of North Carolina at Chapel Hill

REDUCING IMPLICIT ATTRACTIVENESS BIAS

Abstract

Implicit bias affects how people make decisions and judgments. Implicit attractiveness bias is

one strand of this, and this study aims to reduce the amount of this bias in participants. We

predict that increased awareness of implicit attractiveness bias will reduce that bias in

participants that receive the manipulation. Twenty-seven participants, making up the

experimental group, participated in a hiring scenario where they must choose one of three

options to hire: an attractive individual, an unattractive individual, and a faceless avatar. The

experimental and control groups then completed a modified version of the Implicit Association

Test (IAT). With the independent variable being exposure to increased awareness

operationalized through the hiring scenario, the dependent variable was implicit attractiveness

bias. This bias was measured through IAT scores. Our results did not support the hypothesis; the

experimental group did not experience any significantly different reductions in bias than the

control. However, our control results found that females begin with higher bias than males,

which is an important implication for future research. This study suggests that our specific

manipulation was not enough, but that there is still potential for modified versions of increased

awareness or other methods that future researchers can consider.

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Investigating the Effect of Increased Awareness on Reducing Implicit Attractiveness Bias

Implicit bias is a widely studied topic defined as a subconscious feeling or attitude a

person has toward members of certain groups. These groups can be of any type: race, gender,

age, class. For example, many people are biased toward people who are more similar to them in

age or gender (Agthe, Sporle, & Maner, 2011). Because many types of implicit bias are so

prevalent in the population, it is an important topic to study. People are often not aware that their

biases affect the way they behave toward certain groups. Therefore, the more that researchers

know about it, the more they can inform the public about it. The more the public knows about

implicit bias, the more likely they will be to act with awareness of it.

Implicit bias specifically toward people who are regarded as more attractive has

been studied as well. A person is more likely to be biased toward someone who he/she believes

is more attractive than others. This implicit attractiveness bias can be found in many people, but

the standard for what is attractive is not always the same for everyone. This experiment aims to

investigate if an intervention, specifically increased awareness of implicit attractiveness bias, can

reduce this bias. Reducing implicit attractiveness bias could be beneficial to society because

people could learn to evaluate others without taking into account attractiveness and instead take

into account the actual quality of the person in question. We predict that increased awareness of

a person’s attractiveness bias before taking the Implicit Association Test (IAT) will result in a

reduction in their implicit bias toward attractiveness.

Predictors of Implicit Bias

Awareness of one’s implicit bias is a significant predictor of that bias. Particularly, lack

of awareness is a predictor in the way that it leads to uncontrollable bias. Rohner, Walden,

Blomberg, and Carlsson (2013) worked with people in organizations that were considering

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applicants to hire. The study aimed to reduce attractiveness bias in hiring decisions and used

interventions including (1) warning participants about their biases and (2) having the evaluators

rate themselves on their motivation to hire without prejudice. Warning evaluators about their

biases did not result in reduced attractiveness bias, but participants who rated themselves on their

motivation did show reduced bias (Rohner et al., 2013). This study is relevant to the current

experiment because it shows that when participants actively engage with their awareness of

implicit attractiveness bias, such as through rating themselves on how likely they are to hire

without the bias, there is a greater chance of reduced bias. This is also support for the idea that

lack of awareness is a strong predictor of implicit bias. If the current study can apply this

information and increase participants’ awareness about attractiveness bias, the bias should be

reduced. Other predictors of implicit bias include race, gender, age, and education level.

Consequences of Implicit Bias

Relating to attractive people’s organizational success, a study by Agthe, Sporrle, and

Maner (2011) evaluated participants on how likely they were to hire someone or accept someone

to their university after reading qualified cover letters and resumes and looking at a picture of

each applicant. Results found that attractiveness did increase the advantage of applicants (whose

attractiveness was rated before the participants read their letters and saw their pictures) but that

there was more likely to be a negative effect if the evaluator and the applicant were the same sex.

In this way, gender is a significant predictor of implicit bias. Researchers suggested that this

might be due to the feeling of threat if there is a more attractive person working in the

organization (Agthe et al., 2011). If the evaluators had been made more aware of their implicit

attractiveness bias, they might have focused more on the cover letters and resumes instead of

photos of applicants.

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A 2018 study by Dossinger, Wanberg, Choi and Leslie evaluating why there is a positive

relationship between physical attractiveness and salary, researchers wondered if attractive

individuals receive more organizational resources early in their careers. Following 203 employed

recent university graduates over a period of two years, researchers evaluated GPA, major,

demographics, career exposure and access to organizational resources, and salary. Each

participant’s attractiveness was rated twice by a team of professionals. Results found that those

who were more attractive received more experience and exposure from the beginning of their

careers and therefore eventually higher salary (Dossinger et al., 2018). These results suggest that

academic success is not a very accurate predictor of implicit attractiveness bias. This study

suggests an important implication that organizations must be aware of this attractiveness bias

when allocating resources to employees. If increased awareness can reduce implicit

attractiveness bias, organizations could instead give more resources to employees who show the

work ethic to deserve them, whether regarded as attractive or not.

A study by Talamas, Mavor, and Perrett (2016) evaluated how perceived

conscientiousness might be a better predictor of academic success than actual intelligence. This

study relates to attractiveness bias because the researchers acknowledged that attractiveness bias

must be controlled in order to study these variables accurately. Perceived attractiveness is

correlated with perceptions of conscientiousness, but when attractiveness bias is controlled,

researchers found a stronger positive correlation between conscientiousness and performance.

One way researchers controlled for attractiveness bias in this study was excluding all results

from participants who were not white. The faces presented in the ratings were all white and

ratings from other ethnicities were expected to be stereotyped. In this way, the researchers

attempt to control for the fact that race is a predictor of implicit bias. Additionally, this study

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found that attractiveness is not a valid cue to academic performance. Researchers concluded that

people are instead blinded by attractiveness, which affects their perceptions of academic

performances when perceiving people’s faces (Talamas, Mavor, & Perrett, 2016). The current

experiment will add to this study’s idea that attractiveness can stand in the way of how people

perceive others. When perceiving how conscientious a person is, if one can control attractiveness

bias to an extent, that perception might be more accurate.

Another study relevant to the topic of attractiveness bias is by Rohner and Rasmussen

(2011), evaluating how attractiveness is represented in memory and how memory plays a role in

behavior toward people of different levels of attractiveness. Through a process similar to the

IAT, participants were presented with face-word pairs. Words presented included “old” and

“new” among others. Words were positive and negative and evaluated implicit bias through

reaction time. Results of this study found stronger implicit memory for positive words with

attractive faces and negative words with unattractive faces. Memory as a factor in evaluating

attractiveness bias is an interesting topic; results from this study help explain cognitive

mechanisms that are associated with biased behavior around attractive or unattractive people. In

the current study, increasing awareness of attractiveness bias might also affect how memory is

encoded. If participants are more aware of their biases, they might be inclined to process faces in

a different way.

The current study will evaluate whether or not increased awareness of

attractiveness bias will reduce that bias. With current research and predictors of implicit bias in

mind (specifically, lack of awareness, race, and gender), there are many applications of the idea

of increased awareness, such as in a career setting, academic setting or in general evaluation of a

person. The prediction of this study is that increased awareness of a person’s attractiveness bias

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before taking the Implicit Association Test (IAT) will result in a reduction in their implicit bias

toward attractiveness. Through presenting participants with a survey before taking the IAT and

then presenting them with an IAT focused on associating attractive faces with attractive and

unattractive words, the study’s dependent variable will be participants’ association scores.

Through adjusting the awareness participants have of their attractiveness biases, we aim to

reduce attractiveness bias overall. Based on research, especially the study Rohner et al. (2013),

the hypothesis that increased awareness will reduce attractiveness bias is relevant to the broader

study of implicit bias because if we can reduce this bias, we are a step closer to reducing other

types of implicit bias. Additionally, this study will add to what seems to be a small set of

research that investigates successful reductions in attractiveness bias. If the hypothesis of this

study is supported, it will be strong supplemental research to studies such as that by Rohner et al.

(2013).

Method

Participants

Since this research is limited to an online procedure, a convenience sample must be used

so that there can be as many participants as possible. The sample of 54 participants includes

family, friends, classmates, and any other acquaintances willing to give a small amount of time.

27 were assigned to the experimental group, and 27 were controls. Participants range in age,

gender, political ideology, education level, and race. The majority of the participants were aged

18-21 (55.6%). 18.9% were between 22 and 30 years. 13.3% were between 31 and 49. 11.4%

were older than 50 years. For race, 75.9% were white (Hispanic=3.7%, Black or African

American=3.7%, Bi-racial=5.6%, American Indian or Alaska Native=1.9%, Asian=9.3%). 66%

were female, and 34% were male. One person did not report their gender. For political ideology,

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22.2% said they are very liberal, 29.6% said they were somewhat liberal, 24.1% said moderate,

18.5% said somewhat conservative, and 5.6% said very conservative. Overall, this shows that the

majority of our participants were on the liberal end of the spectrum. 64.7% of the participants

have completed some college (Graduated college=13.7%, Completed some graduate

school=3.9%, Graduate or professional degree=15.7%). 55.6% said they have no previous IAT

experience, and 44.4% said they do have IAT experience. Participants were recruited through

individual requests by the members of a research methods course for psychology majors, and

there was no compensation or incentive. Participants gave consent before beginning.

Design

The experimental design employed was a two-group, post-test only. The independent

variable is exposure to awareness of implicit attractiveness bias. The control group did not

receive any information about what attractiveness bias is before. The experimental group

experienced increased awareness of the bias. Both the experimental group and control group first

completed demographic information including sex, ethnic identity, age, political ideology, and

education level. Participants were then randomly assigned into the experimental or control

groups. Following this information, the experimental group moved on to complete a decision-

making task. This task served as the manipulation: increasing awareness of implicit

attractiveness bias. In this way, this survey manipulates attractiveness bias through increasing

awareness of it. We used this hiring scenario targeting guilt to manipulate awareness of implicit

bias in aims to decrease this bias. The dependent variable was implicit attractiveness bias,

measured through an Implicit Association Task (IAT). IAT scores were used to judge

participants’ implicit attractiveness bias. While the experimental group completed the decision-

making task, the control group went straight into the IAT.

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Procedure and Materials

Demographics. Participants were asked their gender, age, race, education level, IAT

experience, and political leaning.

Manipulation. The scenario presented to the experimental group described an employer

who wants to hire a new employee who is kind, trustworthy, agreeable, detail-oriented, and hard-

working (found in Appendix A). We chose this manipulation especially because it relates to

Agthe et al.’s 2011 study about the organizational success of attractive individuals. The

participants then had to choose which of three photos of potential employees was the best for the

job (Debruine & Jones, 2017). One applicant had a face rated “attractive” by independent coders,

one was rated “unattractive,” and the last was an avatar with no face (see all in Appendix B).

After choosing a face, participants were presented with a short text about their decisions. If they

chose the attractive or unattractive face, the text discussed how the participant is likely impacted

by implicit attractiveness bias and how research shows that attractiveness influences hiring rates

(found in Appendix C). If participants chose the avatar, the text also discussed research on

attractiveness bias but will suggest that the participants are likely not impacted by implicit

attractiveness bias in this task. The use of the text explaining attractiveness bias was important

because it exposed the experimental group to increased awareness of their biases, if they

exhibited any.

Implicit Bias. Following the task, the experimental group was presented with eight

“attractive” individuals and eight “unattractive” individuals (found in Appendix D) rated by

independent coders (DeBruine and Jones, 2017). This part of the study served as the control

group’s first step after entering their demographics. The control group had not completed the

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decision-making task at this point. All participants then completed a modified version of the

IAT. IATs aim to measure associations between concepts (Xu, Nosek, Greenwald, Ratliff, Bar-

Anan, Umansky, & Axt, 2019.). In this case, it measures participants’ bias toward attractive

faces. This task first asked participants to use keyboard keys “E” and “I” to say whether or not a

face presented is attractive or unattractive based on when the faces were initially presented.

Participants were then presented with different words that they must label as pleasant or

unpleasant. Next, they were presented with more of the 16 unattractive and attractive faces and

must decide whether they are attractive or unattractive, but the pleasant and unpleasant words are

mixed into this section (Carpenter, Pogacar, Pullig, Kouril, Aguilar, LaBouff, Isenberg, &

Chakroff, in press). This part was meant to measure participants’ implicit attractiveness bias by

recording reaction time and number of errors while associating faces and words.

Finally, participants answered three questions: whether or not they had taken a

reaction time test before, how accurate they thought the labels of “unattractive” and “attractive”

were, and if they noticed anything strange while pairing terms during the IAT. The survey given

to the experimental group was made on Qualtrics. The IAT given to all participants is also on

Qualtrics. Using a Qualtrics survey was the best way to reach as many participants as possible

because it could be sent as a link. Qualtrics is also an efficient way to make clear questions and

tasks that people can easily click through. Participants were required to take the survey on the

computer and not on mobile devices because they needed a keyboard for the IAT. Qualtrics also

gives researchers flexibility to add components they want. For example, the experimental

group’s survey included three images: one reasonably young white woman who was rated as

more attractive by a team of raters, one woman who is also young and white, but was rated as

less attractive, and finally one avatar (a grey shape of a person’s head with a question mark

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where the face would be). These images were parallel to each other on the page and the same

size, so participants could look at all of them at the same time. The specific images were

strategically chosen to represent attractiveness, unattractiveness and no attractiveness.

Results

Primary Analysis

To test whether mean levels of implicit attractiveness bias differed between the control

and experimental groups, we conducted an independent-samples t-test. Results of the two-tailed

t-test indicated that those in the experimental group had the same levels of implicit bias (M = .99,

SD = .25) relative to those in the control group (M = .93, SD = .30), t(52) = .98, p = .334. Since

this value of .334 is not less than 0.05, there is no significant effect of our manipulation, which

aimed to increase awareness, on implicit attractiveness bias. This means that any differences in

implicit bias are due to chance.

Other Inferential Statistics

To determine whether the effect of sex on implicit attractiveness bias differed as a

function of our condition, we conducted a 2 (sex: male, female) X 2 (condition: experimental,

control) analysis of variance (ANOVA). We did not find a significant main effect of condition on

implicit attractiveness bias such that those in the experimental condition had the same level of

bias (M = 1.01, SD = .25) as those in the control group (M = .93, SD = .30), F (1, 49) = 3.26, p

= .077. This p-value suggests our results are marginally significant. We also did not find a

significant main effect of sex on implicit attractiveness bias such that males had the same level of

bias (M = .97, SD = .32) as females (M = .97, SD = .26), F (1,49) = .19, p = .668.

Finally, there was a significant interaction between sex and condition on implicit

attractiveness bias, F (1, 49) = 4.47, p = .040. As shown in Figure 1, when participants were in

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the control group, there was significantly more bias in female participants than male participants.

However, when participants were in the experimental group, there were no significant

differences between male and female implicit bias.

To determine if levels of education predicted implicit attractive bias, we conducted a

multiple regression analysis. Results of the regression analysis indicated that education level was

not a significant predictor of implicit attractiveness bias, b = .01, SE = .03, t(48) = .18, p = .525.

Discussion

We predicted that increased awareness of implicit attractiveness bias would decrease that

bias in participants. This hypothesis was not supported; there was no significant effect of

increased awareness on the IAT scores (bias levels) of participants in the experimental group.

These results do not align with the study by Rohner et al. (2013), which found

significantly reduced bias in participants who rated their own motivation to hire employees

without implicit bias before hiring. These results suggest that increased awareness and

engagement with one’s implicit bias can reduce it. Our study relates to this idea but does not

have matching results. However, our study is different because it did not include an engagement

with bias; participants did not have to rate themselves. Additionally, because our study found no

significantly reduced bias in the experimental groups, this research relates to the results from

Agthe et al. (2011), which found that attractiveness increased the advantage of job applicants.

Our procedure presented a hiring scenario where the experimental group had to choose a face to

hire. There was no significant difference in the experimental group’s bias versus the control

group’s bias. These results align with Agthe et al.’s in the way that they suggest attractiveness

affects hiring decisions. Apart from the experimental groups, our research found that females in

the control group had significantly higher bias than males in the control group. These results

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contradict findings in a study that used the Implicit Relational Assessment Procedure (IRAP), a

similar test to the IAT, to evaluate attractiveness bias in males versus females (Barnes-Holmes,

MacCarthaigh, & Murphy, 2014). This study found significantly greater attractiveness bias in

males than females. Our data suggest the opposite. Our control group results suggest that females

in reality have more attractiveness bias, and it is important to study this further in future research

and compare future results to Barnes-Holmes et al.’s contradictory results.

A major applied implication of our findings is that attractiveness bias is very difficult to

reduce in both males and females. Had our condition successfully reduced bias, we could have

concluded that increased awareness can reduce this bias. However, we cannot conclude this is

the case. Additionally, our findings suggest that females begin with more implicit attractiveness

bias than males. This serves as a theoretical implication allowing new ideas that future research

manipulations should consider. Another theoretical implication of our results is that because the

majority of our experimental group participants picked the attractive face in the hiring scenario,

we can conclude that implicit attractiveness bias might affect hiring situations.

One major limitation of this study is that the images used (find in Appendices A and B)

were white female faces. This could have affected scores of those, such as heterosexual females,

who do not find white female faces as attractive as other groups. This is difficult to solve because

no matter what faces are chosen, there will most likely be a demographic in the sample that is

biased against that face. Another limitation is that participants were required to take the survey

on computers. If the survey had been compatible on mobile phones, it could have reached more

people, yielding a larger sample. A larger sample could have been more representative of the

actual population. This limitation of computers also limits external validity.

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Potential future research should evaluate the idea that females in our control group had

significantly higher implicit attractiveness bias. This trend is important because the control group

represents what is happening in the real population. Future research on implicit bias should use

different manipulations on males versus females to find a way to effectively reduce their biases.

This means that males might need different manipulations than females. Because our results did

not match Agthe et al.’s regarding participants rating their motivation to hire without bias, future

research could also incorporate our use of survey and IAT with Agthe et al.’s use of engagement

with one’s bias. Our research might not have increased awareness quite enough. In other words,

increased awareness of implicit bias could still reduce bias, but it must be greatly increased, even

more than what our study did.

All in all, attractiveness bias will always exist in the population whether people are aware

of it or not. However, there is potential to reduce it if science can continue to build on what it

already knows. One example of this is that our study showed that females begin with more bias

than males. This information should be useful moving forward. Implicit bias as a broader subject

will also always exist. It affects how people make decisions and how they view others. If

researchers can find a way to reduce this bias in any way, people will be able to make more

objective judgments and decisions. For example, managers may be more likely to hire based on

merit instead of being biased toward attractive applicants. Overall, reducing implicit bias will be

helpful in all facets of society. Revised research investigating increased awareness of this bias

could offer reductions.

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References

Agthe, M., Sporrle, M., & Maner, J.K. (2011). Does being attractive always help? Positive and

negative effects of attractiveness on social decision making. Personality and Social

Psychology Bulletin, 37:8, 1042-1054. DOI: 10.1177/0146167211410355

Barnes-Holmes, D., MacCarthaigh, S., & Murphy, C. (2014). Implicit Relational Assessment

Procedure and Attractiveness Bias: Directionalist of Bias and Influence of Gender of

Participants. International Journal of Psychology and Psycholigical Therapy, 14, 333-

351.

Carpenter, T., Pogacar, R., Pullig, C., Kouril, M., Aguilar, S., LaBouff, J. P., Isenberg., N., &

Chakroff, A. (in press). Survey-software Implicit Association Tests: A methodological

and empirical analysis. Behavior Research Methods

DeBruine, L. & Jones (2017): Face Research Lab London Set. figshare.

Dataset. https://doi.org/10.6084/m9.figshare.5047666.v3

Dossinger, K., et al. (2018). The beauty premium: The role of organizational sponsorship in the

relationship between physical attractiveness and early career salaries. Journal of

Vocational Behavior, 112, 109-121. DOI: 10.1016/j.jvb.2019.001.007

Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. (1998). Measuring individual differences in

implicit cognition: the implicit association test. Journal of personality and social

psychology, 74(6), 1464.

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Rohner, J., et al (2013). Reducing physical-attractiveness bias in hiring decisions: An

experimental investigation. Lund Psychological Reports, 13:2.

Talamas, S.N., Mavor, K.I., & Perrett, D.I. (2016). Blinded by beauty: Attractiveness bias and

accurate perceptions of academic performance. PLOS One, 11:2. DOI:

10.1371/journal.pone.0148284.

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Figure 1: Measurement of Interaction Effect Between Sex and Condition

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Appendix A

Appendix B

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REDUCING IMPLICIT ATTRACTIVENESS BIAS 19


Appendix C

Appendix D

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REDUCING IMPLICIT ATTRACTIVENESS BIAS 21

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