When your resume is (not) turning you down:
Modelling ethnic bias in resume screening
|Ann Marie Ryan
Department of Personnel Management,
Work and Organizational Psychology, Ghent
University, Ghent, Belgium
Michigan State University, East Lansing,
Eva Derous, Department of Personnel
Management, Work and Organizational
Psychology, Ghent University, Henri
Dunantlaan 2, Ghent 9000, Belgium.
Resume screening is the first hurdle applicants typically face
when they apply for a job. Despite the many empirical
studies showing bias at the resume‐screening stage, fairness
at this funnelling stage has not been reviewed systematically.
In this paper, a three‐stage model of biased resume screening
is presented. We first discuss relevant theoretical perspec-
tives (e.g., job market signalling and impression formation
theories) to explain why resume screening is vulnerable to
biased decision‐making and ethnic discrimination in particu-
lar. On the basis of the best available evidence, we consider
contingencies of ethnic discrimination in the applicant, the
decision‐maker, and the broader context (e.g., organisation),
as well as the effectiveness of interventions that might
counter ethnic bias in resume screening. The paper ends with
a critical agenda for further research and practice.
discrimination, diversity, ethnicity, recruitment, resume screening
Despite decades of legislation and HR professionals' commitment to equal opportunities, ethnic minority members
still suffer a weaker labour market position compared with equally qualified majorities (Shen, Chanda, D'Netto, &
Monga, 2009). Human capital factors may explain some of the differences in hiring outcomes, but discrimination
has also been put forward as a possible explanation (Derous & Ryan, 2018; Hoque & Noon, 1999). Ethnic minorities,
for instance, still need to complete 50% more applications to get invited for a job interview when compared with
equally qualified ethnic majorities (Zschirnt & Ruedin, 2016).
The review paper is based on a keynote held by the first author at the 18th Conference of the European Association for Work and
Organizational Psychology, May 18, 2017, Dublin, Ireland.
Received: 9 December 2017 Revised: 19 September 2018 Accepted: 21 September 2018
Hum Resour Manag J. 2019;29:113–130. © 2018 John Wiley & Sons Ltdwileyonlinelibrary.com/journal/hrmj 113
Resume screening, the first hurdle applicants typically face seems particularly vulnerable to hiring discrimination.
Fairness in resume screening, however, is less well investigated when compared with the number of studies on the
adverse impact of personnel selection tests (Outtz, 2010). This is remarkable for several reasons. First, resumes are
worldwide one of the most frequently used screening tools. Over 98% of North‐American companies use resume
screening as the first selection hurdle (Piotrowski & Armstrong, 2006). Second, the quantity and quality of the supply
of applicants sets limit on what subsequent HRM practices can achieve (Newman & Lyon, 2009; Thorndike, 1949).
Finally, the influential resource‐based view of the firm (Barney, 2001) spawned several decades of research to illustrate
that HRM practices are important sources of sustainable competitive advantage.Although the HRM literature has inves-
tigated recruitment within this framework, the lack of focus on this critical “entry gate”of resume screening is surprising.
This review focuses on resume screening. Resumes
are applicant‐generated, annotated career summaries of job
qualifications. HR professionals are expected to screen resumes in an objective and fair way based on applicants' job‐
relevant characteristics (like work experiences or educational credentials). Resume screening, however, might be
unfair when resulting in differential treatment discrimination or differential effect discrimination (National Research
Council, 2004). Differential treatment discrimination arises when applicants are treated in an unequal way based
on characteristics related to their group membership (like screening out applicants based on ethnic‐sounding names
as appearing on resumes). Differential effect discrimination results when applicants are treated in an unequal way
based on inadequately justified, nonjob‐related factors that covary with minority characteristics.
The central aim of this paper is to review literature on ethnic bias in the resume‐screening phase so as to inform
HRM practice around this critical point of organisational entry. Below, we present a three‐stage model that integrates
theoretical perspectives to explain why resume screening is vulnerable to biased decision‐making and ethnic discrimi-
nation (Section 2). This section is followed by research findings on contingencies of discriminatory resume screening
and focuses on factors situated at three different levels (i.e., applicant information in resumes, the decision‐maker,
and the broader resume‐screening context) that may moderate biased decision‐making against ethnic minorities
(Section 3). Whereas these first two sections consider microlevel processes, we follow with a critical reflection upon
several practical HRM interventions to avert ethnic discrimination in resume screening that are also situated at different
levels of our model (i.e., the screening tool, the decision‐maker, and the resume‐screening context; Section 4). We
conclude this review with a discussion of limitations, future research opportunities, and implications for HR practice.
2|BIASED RESUME SCREENING
Despite the abundant literature on hiring discrimination, little research has considered why resume screening may be
prone to biased decision‐making. Integrating assumptions from job market signalling and impression formation theo-
ries, we present a three‐stage model on biased decision‐making in resume screening (see Figure 1, Part A). This model
states that when nonjob‐related, stigmatising applicant information is presented in resumes and job‐related,
personalised information is rather limited (Stage 1: Applicant information in resumes), decision‐makers might engage
in categorisation/Type 1 processing (Stage 2: Impression formation), which increases the risk of biased applicant
impressions/ratings and discriminatory decision‐making and—hence—may undermine workforce diversity (Stage 3:
Resume‐screening outcomes). Below, we explain each of the stages in more detail.
2.1 |Stage 1: Applicant information in resumes
The first stage represents the building block of decision‐making in resume screening, namely, applicant information in
resumes, and is based on job market signalling theory. According to this theory (Spence, 1974), hiring managers and
job seekers have partly conflicting interests and will communicate and interpret signals of the other party's unknown
characteristics (like applicants' competencies or the organisation's culture) to obtain the biggest gains (like getting the
best employees on board or getting hired). Typically, signalling theory in selection considers the cues job seekers use
114 DEROUS AND RYAN
to make inferences about prospective employers (Carter & Highhouse, 2014). However HR professionals also look
for signals of applicants' job qualifications, like work experiences in resumes. Besides job‐related information,
resumes might also signal nonjob‐related information, like applicants' social group status, through both explicit and
implicit cues. Applicants' skill sets on resumes, for instance, are explicit/observable signals of applicants' job qualifi-
cations that—at the same time—may also reveal information about applicants' chronological age in an implicit/subtle
way (Abrams, Swift, & Drury, 2016). Similarly, certain extracurricular activities on resumes can subtly signal nonjob‐
related, stigmatising information like applicants' ethnic background (Dovidio & Gaertner, 2000), which might affect
recruiters' information processing.
2.2 |Stage 2: Impression formation
The second stage of the model builds on impression formation theories and represents the way applicant information
is further processed by decision‐makers. Impression formation theories (like the continuum model; Fiske, Lin, &
Neuberg, 1999) specifically explain how signals of applicants' group status affect HR professionals' decision‐making.
When only a limited amount of personalised information is available (like a one‐page resume), individuals will auto-
matically engage in categorisation that in turn may activate group stereotypes. Whereas these processes are auto-
matic or unconscious (i.e., Type 1 processing; Kahneman, 2003), resume screening can also involve high levels of
conscious involvement, such as assessments of the congruence of applicants' characteristics with job and organisa-
tion characteristics (i.e., fit; Kristof‐Brown, 2000). When more personalised information becomes available, recruiters
might engage in attribute‐based processing instead of category‐based processing of applicant information (i.e., Type
2 processing; Kahneman, 2003). The point, however, is that Type 1 processing readily occurs during resume
screening because of the limited amount of applicant information and, hence, may colour resume‐screening outcomes.
2.3 |Stage 3: Resume‐screening outcomes
The third stage of the model focuses on the outcomes that result from the information processing stage during
resume screening. Specifically, Type 1 processes may affect decision‐makers' first impressions/ratings and trigger
FIGURE 1 Model of biased resume screening [Colour figure can be viewed at wileyonlinelibrary.com]
DEROUS AND RYAN 115
discriminatory decision‐making. Perceptions of similarity, for instance, may automatically induce interpersonal
attraction (Byrne, 1961) and explain why recruiters/organisations tend to attract, select, and retain applicants
that are similar to job incumbents (Schneider, 1987). Such cognitive processes may lead towards homogeneous
workforces and may undermine organisational diversity. As we will delineate more in discussing interventions,
when more personalised information becomes available about an applicant, HR professionals should be better
able to monitor Type 1 processing, which may result in a more fully informed and unbiased decision about the
The three‐stage model illustrates why resume screening is vulnerable to biased decision‐making (see Figure 1,
Part A),but not why ethnic discrimination occurs. Several social‐economic and psychological theories may further
clarify why HRM systems may lead to such discrimination. Typically, social‐economic theories stress macrolevel fac-
tors, like resource availability, institutional ideologies, industry culture, and local labour market practices (Almeida,
Fernando, & Sheridan, 2012; Béret, Mendez, Paraponaris, & Richez‐Battesti, 2003; Blair, Culkin, & Randle, 2003;
Sidanius & Pratto, 1999). Social‐psychological theories discuss microlevel factors; among which are demographic dis-
similarity to others (Goldberg, 2005), individuals' need to protect their in‐group and self‐identity (Tajfel & Turner,
1979), stereotypes and prejudiced attitudes (McConahay, Hardee, & Batts, 1981), and personality and dispositions
that trigger prejudice (Altemeyer, 1981; Sidanius & Pratto, 1999). Table 1 describes and illustrates some of the most
cited theories on ethnic discrimination in hiring.
3|CONTINGENCIES OF BIASED RESUME SCREENING
Resume screening may be more prone to ethnic discrimination when applicants' ethnic minority status is cued in
resumes and job‐related, personalised information is limited (Stage 1). Decision‐makers, however, may not be equally
affected by applicant information in resumes (Stage 2). Whether they engage in categorisation or move to more
individualised information processing and decision‐making (Stage 3) might depend on several contingencies that acti-
vate or inhibit categorisation (Kulik, Roberson, & Perry, 2007) and that are situated in the applicant (ethnic markers,
qualifications), the decision‐maker (beliefs, attitudes, experience), and the broader screening context (screening task,
job, organisation, society). Below, we review contingencies that are discussed in the literature and that may moderate
impression formation and resume‐screening outcomes (see Figure 1, Part B).
3.1.1 |Ethnicity cues
Applicants' ethnic‐sounding names are one of the most investigated and explicit ethnic markers on resumes. In their
seminal correspondence audit study,
Bertrand and Mullainathan (2004) showed that resumes with African‐American
sounding names received 50% less of a chance of a positive callback compared with those with White‐sounding
names. Ethnic name discrimination in resume screening has been reported around the world (see Zschirnt & Ruedin,
2016, for a meta‐analysis), and diverse lab studies also showed applicants with ethnic‐sounding names to be least
liked and hired (e.g., Cotton, O'Neill, & Griffin, 2008). Yet resumes may even include more explicit ethnic markers, like
cues about one's appearance in pictures attached to resumes. Research, for instance, shows an overall preference for
light over dark‐skinned applicants, even among darker skinned recruiters (Harrison & Thomas, 2009). Because visual
cues are immediately available and more rapidly processed than verbal and behavioural cues, visual markers of one's
ethnic background (like skin tone) might trigger social categorisation more than ethnic‐sounding names
(Weichselbaumer, 2017). Although it is common to ask applicants for their picture in some countries (Belgium), in
others, it is not (like the Netherlands). Nevertheless, trends towards pre‐screening via social media (SHRM, 2016)
make ethnic markers (like skin tone, ethnic attire, and even speech) more salient in early screening stages and there-
fore potential for bias should be considered.
116 DEROUS AND RYAN
TABLE 1 Socio‐economic and psychological theories on ethnic discrimination in hiring
Theories (domain/level/description) Illustration
Majority workers/employers avoid interacting with minorities
because of taste‐based factors instead of ascribed
productivity (preference‐based hiring).
Hiring managers set different group‐based thresholds of job‐
irrelevant characteristics (like ethnic accent), leading to lower
ability estimations of minority members (i.e., taste‐based
discrimination). Statistical discrimination occurs when
observed group differentials in productivity are mistakenly
attributed to group identity (Neilson & Ying, 2016)
Majority workers/employers disavow minority individuals
because they consider minority groups as a whole less
productive from an economic point of view.
Societies share ideologies that justify group‐based
inequalities and formally/informally embedded
societal/organisational processes support such ideologies.
Hiring based on employee recommendations instead of
advertisements leads to differential effect discrimination if
employees systematically recommend applicants that are
similar to themselves (Waldinger & Lichter, 2003).
Group position theories,
realistic group conflict,
Social groups are in competition over valued resources and
perceived threats from loss of resources results in
Social‐economic threat (like recession) fostered hiring
discrimination against ethnic female applicants (King, Knight,
& Hebl, 2010).
Majority members compare their demographic characteristics
to minority members. Perceived dissimilarity leads to
negative attitudes and behaviour towards minority
Race similarity effects were observed on candidates' overall
interview assessments and job offer decisions by White
recruiters (Goldberg, 2005)
Social categorisation and
social identity theory
Majorities' need for and protection of a positive identity (self/
group) might instigate in‐group favouritism, which may
lead to discrimination against minorities
Resumes of highly ethnic‐identified applicants received lower
job suitability ratings than equally qualified but less ethnically
identified counterparts (Derous, Nguyen, & Ryan, 2009).
Stereotypes lead to discrimination. Stereotypes are
majorities' individual beliefs (cognitive schema and
expectations) about group characteristics that are applied
to minority and that are typically (but not necessarily)
negative in nature. Minorities may be aware of majorities'
stereotypical beliefs about them (i.e., meta‐stereotypes)
and even integrate these ideas into their self‐concept (i.e.,
Determining who gets hired for which job type depends on
applicants' match to the job stereotype, like applicants from
stereotypically warm groups hired more for social jobs
(Cuddy, Glick, & Beninger, 2011). (Negative) Meta‐
stereotypes might withhold qualified minorities from
applying for certain jobs (Wille & Derous, 2017).
Theories on old‐fashioned
racism, modern racism,
and motivation to
Racial/ethnic prejudice are racial/ethnic majorities'
predominantly negative attitudes towards racial/ethnic
minorities that might result in either blatant or more subtle
forms of discrimination against these minorities or
Modern racism affected hiring discrimination when a legitimate
authority figure provided a business‐related justification for
discrimination (Brief, Dietz, Cohen, Pugh, & Vaslow, 2000).
Theories on personality
Personality underlying majorities' worldviews affecting
ethnic/racial prejudice reactions towards minorities are
right‐wing authoritarianism (conformity to tradition,
submission to powerful leaders, and political orientation),
and social dominance orientation (endorsement of group
Majority of the members high in social dominance orientation
(SDO) were less likely to select a potential team member
from low‐status groups (like racial minority candidates) than
those low in SDO (Umphress, Simmons, Boswell, &
DEROUS AND RYAN 117
Although studies typically focus on explicit markers on resumes, resumes also contain more implicit cues to one's
ethnicity, such as applicants' affiliations with socio‐cultural groups, that may trigger biased information processing.
Cole, Rubin, Feild, and Giles (2007) illustrated that although HR professionals believed work experience to be the
strongest influence on ratings of applicants' employability, ratings were mostly affected by affiliations as mentioned
on resumes. Moreover, multiple ethnic cues may also interact and increase category salience such that resumes of
highly ethnically identified applicants (e.g., with ethnic‐sounding name and affiliations) might receive lower employ-
ability ratings due to increased out‐group status (Derous et al., 2009; Kang, DeCelles, Tilcsik, & Jun, 2016).
Job‐related cues on resumes typically temper categorisation. Contemporary models of discrimination (Dovidio &
Gaertner, 2000) suggest less discrimination if applicants either clearly possess the requested job qualifications or
do not possess them at all. However, when qualifications are moderate, a more ambiguous situation is created in
which discriminatory hiring decisions could be justified. For example, Almeida et al. (2012) noted that a lack of
recognition of experience and credentials gained overseas as well as concerns about language skills affected the
employment outcomes of professional immigrants. Hence, ethnic discrimination may occur more when decisions
can be rationalised based on some other factors than applicants' ethnicity (Brief et al., 2000).
Studies on ethnic bias in resume screening have somewhat disregarded individual differences in decision‐makers' per-
sonality, beliefs about others (worldviews/stereotypes), prejudiced attitudes, and recruiting experience. This may be
explained by the long‐standing tradition of audit studies in which decision‐makers' dispositions are typically not
accessible (for an exception, see Rooth, 2010). Studies that measure individual differences are predominantly con-
ducted in the lab and include beliefs in societal group hierarchies, like social dominance orientation (Sidanius& Pratto,
1999), prejudiced attitudes like modern racism (McConahay et al., 1981), and motivation to respond without preju-
dice (Plant & Devine, 1998, 2009). Although findings are sometimes mixed, negative beliefs/attitudes towards others
typically result in larger ethnic bias (Derous & Ryan, 2018). Indeed, although blatant discrimination is still reported,
research also shows that recruiters may not act upon their prejudice in resume‐screening situations where bias would
be very obvious (e.g., if directly attributable to themselves) or when there is an external reason not to react in a
biased way (e.g., because of organisational policies; Brief et al., 2000).
Because individuals have become more sensitive to politically correct standards to disavow discrimination
(Dovidio & Gaertner, 2000), researchers directed their attention to implicit prejudice. Rooth (2010), for instance,
showed that Swedish recruiters were less likely to invite Arab‐Muslim minority applicants when they had more
negative, implicit attitudes towards Arab‐Muslims (as measured with implicit association tests; Greenwald, Banaji,
& Nosek, 2015
). There is, nevertheless, an ongoing debate about the validity of implicit attitude measures and
whether any relation with behaviour can be expected and established outside the lab (Oswald, Mitchell, Blanton,
Jaccard, & Tetlock, 2015).
Finally, there is also a debate about the role of decision‐makers' expertise. Predominantly discussed in interviewing
studies, some show more experienced recruiters to be less biased towards stigmatised applicants, whereas others show
more bias. De Meijer, Born, van Zielst, and van der Molen (2007), for instance, found that experienced recruiters used
more irrelevant information when judging ethnic minorities, perhaps because they feel overconfident and hence,
engage in Type 1 processing.
Contextual cues, like job and organisation characteristics as well as the way decision‐makers perform resume‐
screening tasks, also affect impression formation and resume‐screening outcomes.
118 DEROUS AND RYAN
3.3.1 |Job characteristics
Contextual factors studied most are job stereotypes. Stereotypes not only exist for people but also for jobs, and
these may orient HR professionals towards viewing applicants as more or less suitable for certain jobs (i.e., cognitive
matching model; Trope & Liberman, 1993). For instance, King, Mendoza, Madera, Hebl, and Knight (2006) showed
that the effect of applicants' ethnic names on overall resume evaluation was not significant when applicants'
suitability for high—versus low—status jobs was controlled for, suggesting job stereotypes affected resume‐screening
outcomes. Audit studies (Carlsson & Rooth, 2008) further demonstrated lower callback ratios for ethnic minorities
who applied for occupations with higher external client contact (restaurant workers and shop sales assistants). Yet
mixed findings are also reported (Booth, Leigh, & Varganova, 2012; Derous, Ryan, & Serlie, 2015; Weichselbaumer,
2017), perhaps because matching effects may depend on a mixture of contextual cues (Goldberg, Finkelstein, Perry,
& Konrad, 2004). For instance, Dietz, Joshi, Esses, Hamilton, and Gabarrot (2015) showed that bias against qualified
immigrants was mitigated when the fit with the clientele was emphasised. Derous, Pepermans, and Ryan (2017) fur-
ther showed that discriminatory resume screening of the same applicant with varying skin tone (dark vs. light)
depended on the particular combination of several job and industry characteristics.
3.3.2 |Organisation/task characteristics
Aside from some studies that considered the demographic diversity of organisations and their clients (Almeida,
Fernando, Hannif, & Dharmage, 2015), surprisingly little research considers the role of other organisational character-
istics (like size and policies) and screening task characteristics (like time and financial pressure) on the way HR profes-
sionals screen resumes (Almeida et al., 2012). However, the HRM literature has established that line managers do not
fully engage in HRM duties because of time pressures and prioritising operational over HR tasks (McGovern, Gratton,
Hope‐Hailey, Stiles, & Truss, 1997; Woodrow & Guest, 2014). Such time and motivational constraints likely
contribute to using categorisation to move through resume screening more quickly.
In sum, screening out competent people during the resume‐screening stage because of nonjob‐related applicant
factors, decision‐makers' dispositions, and contextual factors is worrisome and costly, especially when labour markets
are tight and talented workers are hard to find. Hence, effective interventions are much needed to avert discrimina-
tory resume screening.
4|INTERVENTIONS TO AVERT BIASED RESUME SCREENING
Understanding contingencies may help both researchers and practitioners evaluating selection practices as well as
interventions to mitigate biased decision‐making. Based on the best available evidence, this section critically dis-
cusses interventions to avert discriminatory resume screening that are situated at the level of the screening tool,
the decision‐maker, and the resume‐screening context (see Figure 1, Part C).
4.1 |Screening tool
At the level of the screening tool, three different types of interventions are discussed, namely, anonymisation,
personalisation, and standardisation.
Anonymous application procedures such as blind auditions (Goldin & Rouse, 2000), blind interviewing (Buijsrogge,
Derous, & Duyck, 2016), anonymous resume screening (Åslund & Skans, 2012), or “whitened”
et al., 2016) aim to combat illegal discrimination by blotting or concealing personal identifiers. Although blind
auditions and interviewing have been found to be effective, studies on anonymous resume screening have shown
positive (Åslund & Skans, 2012; Kang et al., 2016), null, or even negative effects (Behaghel, Crépon, & Le Barbanchon,
DEROUS AND RYAN 119
2015; Hiscox et al., 2017; Krause, Rinne, & Zimmermann, 2012). The French government, therefore, decided to aban-
don the idea of making anonymous resume screening mandatory in the recruitment procedures of their public
employment services (Behaghel et al., 2015).
Why may anonymous resume screening fail? Both signalling and impression formation theory can help us under-
stand unintended side effects of anonymous resume screening. Aside from very explicit markers, resumes might also
contain more implicit cues, such as extracurricular activities, that might signal in a subtle way applicants' ethnic
minority status (Dovidio & Gaertner, 2000). Further, with anonymous resume screening, resumes are
decontextualised and depersonalised. As a result, HR professionals have less possibility to understand and attenuate
negative signals (e.g., from gaps in resumes or lower qualifications; Behaghel et al., 2015) and therefore might—par-
adoxically—engage in categorisation.
Instead of altering or removing information, applicants could provide more personalised information, for instance, by
means of video resumes or social network sites. Video resumes are short videotaped messages of 1–2 min in which
an applicant presents himself/herself to potential employers. Much like paper resumes, video resumes present candi-
date information in an asynchronous way (one can view the resume information at any time, at any place). However,
they differ from paper resumes in that they provide more and different cues and allow applicants to show relevant com-
petencies. Interestingly, although ethnic minority applicants consider video resumes as more fair than paper‐and‐pencil
resumes, HR managers report concerns as more nonjob‐relevant information (like physical attractiveness) is included
(Hiemstra & Derous, 2015).
Applicants may also provide more personalised information through social media as individuals increasingly
include links to their social network sites on their resumes (SHRM, 2016). HR professionals may use “cybervetting”
(i.e., the screening of social media sites like Facebook and LinkedIn) to extract information from applicants to inform
personnel decisions (Berkelaar & Buzzanell, 2014). About 44% of HR managers believe candidates' public social
network sites to be good sources for assessing potential (SHRM, 2016). Indeed, social network information might
provide more and different types of personalised information (like interests, values, and interactions with other users)
that reflect more typical behaviours than resumes do. Therefore, these sources might have incremental validity
beyond traditional screening tools.
However, findings are inconclusive about the validity of social network information. Kluemper, Rosen, and
Mossholder (2012) found that personality traits could be reliably assessed via Facebook profiles and were predictive
of future work behaviour beyond applicants' self‐rated personality and intelligence scores. Yet Van Iddekinge,
Lanivich, Roth, and Junco (2016) showed that across a broad array of KSAOs, ratings of applicants' Facebook pages
did not predict job performance (i.e., supervisor ratings, turnover intentions, and actual turnover). Moreover, HR
professionals tended to favour White and female applicants when they screened applicants' Facebook information,
resulting in adverse impact. Furthermore, the availability of job irrelevant information may impair the overall validity
of unstandardised social media despite the fact that typical performance might be reflected in these media.
Given that standardisation of selection procedures reduces the chance of judgmental biases in both recruiters and
applicants (Highhouse, Doverspike, & Guion, 2015), structured application forms might also be considered. This allows
organisations to score applicants' competencies and background information in a more objective way than with
applicant‐generated resumes that lack uniformity. Standardised application forms may also provide applicants fewer
possibilities to use impression formation tactics and faking than applicant‐generated resumes (Derous & Ryan,
2018). Equally, more structured, job‐related social network sites like LinkedIn might make these sources less
vulnerable to biased decision‐making than less structured media and at the same time increase their validity.
Corroborating this, van de Ven, Bogaert, Serlie, Brandt, and Denissen (2017) recently showed accurate personality
120 DEROUS AND RYAN
estimates based on LinkedIn profiles. However, the effectiveness of screening tools will also depend on
decision‐maker's characteristics and the way the decision‐maker uses the tool.
This section considers the feasibility and effectiveness of four different types of interventions focused on the deci-
sion‐maker, whether in HR or line management: selecting out prejudiced raters, offering training, holding raters
accountable, and replacing human decision‐makers with algorithms.
Selecting out prejudiced raters seems obvious given effects of raters' particular worldviews (like social dominance ori-
entation) and prejudiced attitudes on judgments (i.e., theories on modern racism and authoritarian personality; see
Table 1). This intervention, however, might not be feasible as those chosen to screen resumes might do so because
of their technical expertise or hiring authority (Brief et al., 2000). Indeed, globally, HRM responsibilities related to
selection are increasing the responsibility of line managers rather than HR professionals (Brewster, Brookes, & Gollan,
2015). Furthermore, explicit prejudice measures may be susceptible to socially desirable responding, and their predic-
tive validity in the context of choosing resume screeners still needs to be demonstrated. The same applies to other
measures of individual predispositions, like social dominance orientation. Therefore, other interventions like training
Recruiters could be trained to increase awareness about judgmental biases in resume screening. Dietz et al. (2015),
for instance, demonstrated how developing a common identity across groups may be a basis for inclusive HRM strat-
egies and reduce hiring discrimination against high skilled immigrants, for example, when a fit with a diverse clientele
is emphasised (i.e., common in‐group identity model; Gaertner & Dovidio, 2000). Building on social psychological the-
ories on categorisation, stereotyping, and motivation to respond without prejudice (see Table 1), Devine, Forscher,
Austin, and Cox (2012) further showed evidence for a multi‐faceted implicit prejudice habit‐breaking intervention
that lasted 8 weeks and included different elements such as contact, perspective taking, stereotype replacement
(i.e., reconsideration of actions and thoughts to replace biased response), counter‐stereotypical imaging (i.e., imagin-
ing examples of out‐group members who counter commonly held stereotypes), and individuating (i.e., considering
out‐group members as individuals instead of stereotyped group members). However, these interventions are typically
developed for and tested in educational settings and not yet in corporate contexts, like resume screening.
Holding recruiters accountable for their decisions could also hold them from acting in prejudiced ways. However,
Self, Mitchell, Mellers, Tetlock, and Hildreth (2015) showed that type of accountability instruction matters. Holding
people accountable for certain outcomes, like an increase in the representation of minority applicants to face legal
or other pressures (i.e., identity‐conscious accountability), resulted in more pro‐minority bias and less qualified
applicants than when recruiters were held accountable for making fair selection decisions based on job‐relevant con-
siderations (i.e., identity‐blind accountability).
Panel recruitment in which a team instead of a single rater screens resumes may be another avenue to increase fair-
ness. Followingpredictions from contemporarytheories on prejudice (seeTable 1), the presence of significant others (like
colleagues) might externally motivate recruiters to respond without prejudice and—hence—to avoid being perceived as
discriminatory and/or to avoid repercussions (Plant & Devine, 1998, 2009). Ethnically mixed screening panels might
even lead to less biased decision‐making. When recruiters work in ethnically mixed screening panels, they might get
to know each other and, as a consequence, might move from social categorisation (Type 1 processing) to
individualisation (Type 2 processing) (Fiske et al., 1999; Kahneman, 2003). Building further on predictions from the social
DEROUS AND RYAN 121
identity theory, ethnic minority and majority recruiters might even develop a common in‐group identity, which also
reduces the chance on biased decision‐making (i.e., common in‐group identity model; see Gaertner & Dovidio, 2000).
In general, HRM research has clearly established that HR departments play a key role in enabling line managers
to successfully implement effective HR practices (Trullen, Stirpe, Bonache, & Valverde, 2016). Creating accountability
as well as providing recognition for unbiased hiring can be an important lever in ensuring effective resume screening.
Instead of screening, training, and making decision‐makers accountable for fair screening,one could also replace
human decision‐makers by automated resume readers or algorithms. This idea is not new: In the 1970s, both the
Pentagon and IBM already replaced human decision‐makers by algorithms to narrow down the large piles of resumes
(O'Neil, 2016). Automated resume readers may boost efficiency by saving time, money, and energy. The French cos-
metic company L'Oréal, for instance, developed an algorithm to measure cultural fit based on only three open‐ended
questions candidates answered on their mobile phone, which released recruiters from the time‐consuming procedure
of screening many resumes.
Proponents argue that algorithms may be more accurate and predictive than human decision‐makers (Danieli,
Hillis, & Luca, 2016). Although professionals still prefer holistic information processing (Kuncel, Klieger, & Ones,
2014), Kuncel, Klieger, Connelly, and Ones (2013) showed that mechanical data combination methods resulted in
more than 50% improvement in the prediction of work and academic criteria when compared with more holistic, intu-
itive methods. Other researchers further showed that algorithms can rate applicants' accomplishment narratives as
reliably as human raters (Campion, Campion, Campion, & Reider, 2016), can predict applicants' personality traits
and social/communication skills reasonably well from nonverbal cues extracted from video resumes (Nguyen &
Gatica‐Perez, 2016) or from Facebook likes (Youyou, Kosinski, & Stillwell, 2015), and can even predict which candi-
dates would most likely become involved in shooting or be accused of abuse as police officers (Chalfin et al., 2016).
Still, opponents remain cautious about the overall validity and fairness of automated resume‐screening tools: If
people have the ability to identify how algorithms work, they might beat them too through strategic behaviour (like
drafting resumes to fit the system). Although some biases like friendship bias (Nguyen, 2006) might be countered,
automated resume screening might still be vulnerable to impression management and even faking behaviour (Waung,
McAuslan, DiMambro, & Mięgoć, 2017) as it might be as difficult for algorithms as human decision‐makers to filter
this out. Moreover, when algorithms are built upon human decision‐makers' subtly prejudiced rules, they might be
even more precise and persistent in discriminatory decision‐making than any human decision‐maker. For instance,
Saint George's Hospital Medical School of South‐London was found guilty of discrimination in its admission policy
because their automated resume reader used nonjob‐related criteria (like misspellings), which were correlated with
applicants' ethnic group membership (Lowry & MacPherson, 1988).
In addition to interventions in the resume‐screening tool and with decision‐makers, organisations as well as society at
large could develop policies and procedures to record discriminatory screening practices, to monitor recruitment
messages/sources and to guarantee competence‐based assessments through discrimination‐free employment
Organisations could use different techniques, like correspondence audits (see earlier) and mystery shopping tests, to
measure and record hiring discrimination at the organisational and industry level. Mystery shopping involves a con-
federate who makes checks against specified criteria in order to get insight into system delivery. The self‐regulating
body of recruitment offices in Flanders, for instance, had fictitious commissioning clients deliberately ask discrimina-
tory questions to recruitment offices in order to uncover discriminatory intentions (Federgon, 2013). Similar research
122 DEROUS AND RYAN
has asked subsidised cleaning companies to send out only native, Belgian cleaners to potential employers. Whereas
correspondence audits register actual discrimination, mystery shopping only capture one's intention to act in a dis-
criminatory way. Hence, one point of debate is whether mystery shopping might be used in a punitive rather than
a self‐monitoring way. Also, discriminatory intentions might reflect many different underlying, biasing processes that
are typically not directly measured with these tools (e.g., preferences, beliefs about economic productivity and com-
petitiveness, and social dominance; see Table 1). Another point of discussion is who may administer such tests,
whether to encourage HR managers and CEOs to organise audits and mystery shopping themselves or to consider
using qualified research institutes and/or governmental bodies.
4.3.2 |Targeted recruitment
Organisations may also attract more minority job seekers through targeted recruitment strategies like diversity state-
ments and the portrayal of minority employees in job advertisements. These targeted recruitment strategies build on
the social identity theory: Applicants who perceive the best fit with their social/individual identity may feel most
attracted to the organisation and may apply. Hence, by increasing the number of ethnic minority applicants that
apply, targeted recruitment strategies may be a way to avert adverse impact and to increase fairness in assessment.
Though, because effects of such targeted recruitment strategies on the reduction of adverse impact are rather mixed
(Avery & McKay, 2006), researchers turned their attention towards qualification‐based targeted recruitment strategies,
aimed to attract highly qualified ethnic minorities. Newman and Lyon (2009) indeed showed that job postings
designed to attract highly qualified ethnic minorities (e.g., requiring applicants high in conscientiousness) resulted
in less adverse impact. However and although promising, qualification‐based targeted recruitment strategies still
tend to disregard stereotypical ideas applicants might have about job qualifications/requirements. Indeed, applicants
too might have ideas about the stereotypical beliefs out‐group members hold about in‐group members (i.e., meta‐
stereotypes; Vorauer, Main, & O'Connell, 1998), and they may even integrate such meta‐stereotypes into their
own self‐concept (self‐stereotyping). Building further on stereotype content models, Wille and Derous (2017)
showed that organisations should be cautious about sprinkling job ads with requirements that (minority) candidates
hold negative meta‐stereotypes about, particularly if those requirements are communicated in dispositional ways
(like “This company is looking for applicants who are high in integrity”). Such job ads might discourage (highly
qualified) minority candidates to apply instead of attracting them.
Besides recruitment messages, organisations may also consider their recruitment sources as some might be less
frequently consulted/used by minority than by majority job seekers. For instance, video resumes are potentially dis-
criminatory against minority groups who may have less tech access (i.e., differential effect discrimination; Heathfield,
2016). Remarkably, bias might even be encoded in algorithms of search engines (Hajian, Bonchi, & Castillo, 2016).
Sweeney (2013) showed that algorithms for public record websites were more likely to imply criminal activities (like
arrest records) with searches for Black‐sounding names than White‐sounding names. Finally, labour market interme-
diaries (temporary work agencies and public employment services) can play a role in assuming some level of recruit-
ment and selection functions for hiring organisations (Bonet, Cappelli, & Hamori, 2013). However, Ingold and
Valizade (2017) demonstrated that although intermediaries may increase likelihood of hiring from disadvantaged
groups, employer selective hiring criteria still led to lower employability of marginalised groups.
4.3.3 |Employment (economic/societal)
Finally, more radical interventions consider the rethinking of employment relations at the economic/societal level to
reduce hiring discrimination by promoting open,accessible labour markets. One way to realise this is through new
types of employment arrangements. eLancing
(Aguinis & Lawal, 2013) might address this call: Employers' evaluation
of eLancers based on their past assignments resembles work sample tests that are known to be valid predictors of
future work performances. Furthermore, hiring for “eLancing”assignments may be blind, so that freelancers' ethnicity
does not affect decision‐making.
DEROUS AND RYAN 123
Open Badges ecosystems are another way to create more accessible, discrimination‐free labour markets. The open
badges ecosystem (https://openbadges.org), originally launched by Mozilla, encompasses a method for packaging
information about one's individual accomplishments, skills, qualities, or interests in portable image files as a digital
badge that subsequently can be displayed via job seekers' social media platforms and consulted by potential
employers. The system's infrastructure ensures that badges are reliably issued by institutions and endorsed within
the open badges ecosystem (e.g., as approved by the Department of Education or other reliable institutions). Through
open badges backpacks, applicants might provide potential employers with very personalised, timely, job relevant,
and certified/objective information about their competencies during the initial screening stage, which in turn might
help countering social categorisation and hiring discrimination. Indeed, according to impression formation theories
(e.g., Fiske et al., 1999), the more personalised information a recruiter/HR professional receives about an applicant
in the early screening stage (e.g., through information in open badges), the more she/he might engage inType 2 pro-
cessing (individuating) and move away from Type 1 processing (social categorisation).
Technological developments (like Open Badges) not only offer alternatives for discriminatory resume screening
but also redesign HR practices fundamentally. Whereas traditionally, companies attract, screen, and select applicants
by presenting job requirements/offers, through technological developments like open badges, the power nexus shifts
to the applicant, who will attract, screen, and even select companies/jobs by showing their competencies (i.e., com-
panies bidding for applicants).
Diversity in organisations can be effectively managed through HRM practices. Remarkably, despite societal debates
about fair hiring (Feintzeig, 2016), fairness of HR tools like resume screening has received less research attention,
especially when compared with the extensive literature on other selection tools, like the job interview. Resume
screening, however, is worldwide one of the most frequently used screening tools that determines the quantity, qual-
ity, and diversity of applicant pools. We aimed to address this gap by formulating a model of biased decision‐making
in resume screening (Figure 1) that includes contingencies of resume screening as well as interventions to avert dis-
criminatory screening,all related to relevant theories and empirical findings on ethnic discrimination. It further allows
to identify mixed findings and literature gaps. Hence, the model might steer further research on discriminatory
resume screening as well as interventions to avert this. Below, we summarise the most important opportunities for
further research, followed by implications for practitioners.
5.1 |Research opportunities
5.1.1 |Applicant information and screening tool
At the heart of the model (Figure 1, Part A) is a cognitive mechanism of biased decision‐making that we based on
assumptions from job market signalling and impression formation theory. Research could further investigate
microlevel processes of impression formation in a bottom‐up way, for example, by tracing decision‐makers' attention
to both nonjob‐related/job‐related and implicit/explicit information, by investigating whether attention paid to
different resume cues differently affects categorisation/individualisation, and by investigating their effect on
resume‐screening outcomes (see for a similar approach on interview bias: Buijsrogge et al., 2016). These findings
might also provide useful information regarding the effectiveness (validity) of anonymisation versus personalisation
of resumes. Related, more empirical studies are needed on the effectiveness of structuring applicant information
to avert Type 1 processing in decision‐makers. Further and as already mentioned, we considered ethnic discrimina-
tion. Systematic reviews of effects of other stigmatising cues in resumes (and combined effects) are also needed,
given the paucity of reviews on judgmental biases in resume screening and the necessity to generalise findings to dif-
ferent stigmatised groups.
124 DEROUS AND RYAN
Surprisingly little research considers individual difference variables that make decision‐makers vulnerable to biased decision‐
making in the resume‐screening stage. Hence, more research is needed on reliable/valid methods of choosing resume
screeners for unbiased decision‐making (like measures of prejudiced attitudes), on training programs that might change
recruiter bias in resume‐screening contexts, and on the usefulness of ethnically mixed screening panels. Also, researchers
have only started evaluating the way algorithms are developed and validated. As human decision‐makers are already being
replaced by algorithms in organisations, algorithms should be compared with humans regarding reliability/validity of
decisions, levels of adverse impact, vulnerability to impression management (including faking), and perceived fairness.
Not only individual difference variables but also contextual variables may increase the likelihood of Type 1 processing
and trigger biased decision‐making in resume screening. Future research could consider moderating effect of
microlevel factors like job and resume‐screening task characteristics (like available time and other task‐related pres-
sures). For instance, some recruitment sources do not reach potentially qualified candidates from ethnic minority
communities or might discourage qualified applicants to apply. However, also mesolevel factors (like organisational
diversity policies) and macrolevel factors (like labour market situation, work arrangements, politics, and cultural
habits) might affect recruitment practices and should be further considered. For instance, affirmative action plans
cannot be realised with anonymous resume screening as one needs to be aware of social category membership. As
regards macrolevel factors, one could further investigate whether effectiveness of Open Badges depends on the kind
of information included in badges (like cognitive test performance).
5.2 |Practical implications
Organisations may keep track on how decision‐makers evaluate applicants and could set‐up specific training pro-
grams in which recruiters are informed about judgmental mechanisms and biases (e.g., induced by cultural differ-
ences) as well as potential effective interventions to avert biases, such as the use of qualification‐based targeted
recruitment and competence‐based screening tools like structured application forms. However, targeted recruitment
initiatives as well as more technology‐driven applications (like automated resume‐screening tools) should always be
critically evaluated to assure they are valid and free from bias, regardless of whether they are developed outside or
inside one's organisation. Relatedly, organisations should keep up to date about, set‐up, and communicate their pol-
icies on cybervetting so that both recruiters and applicants are fully aware of the kind of information that might be
evaluated online. Finally, more attention should be paid to recruiters/decision‐makers' working conditions. Stress
levels (due to time or any other task‐related pressure) should be reduced as these might increase the risk of Type
1 processes and biased decision‐making in resume screening.
Ethnic minority applicants and career counsellors canbenefit too from literature insights. For instance, applicants may
be informed about explicit/implicit cues to both job‐related and nonjob‐related information on their resumes as well as
about organisational context factors (like client preferences). Career counsellors might also help applicants to properly
interpret job requirements and check critically whether applicants' qualifications are not too ambiguously presented but
clearly match the job requirements to minimise riskson discrimination. Finally, social media profiles may be kept up to date
and best include professional information only. In general, applicants might consider recruitment devices that allow for
more competence‐based, individualised screening (like badges, structured competency lists, or perhaps video resumes).
In conclusion, despite the widespread use of resume screening as well as the plethora of studies on ethnic discrim-
ination in hiring, a model on biased decision‐making in resume screening that integrates findings was still lacking.
DEROUS AND RYAN 125
One of the strengths of this paper is that we addressed this literature gap by highlighting an underlying mechanism of
biased decision‐making, contingencies that might moderate bias, and interventions that might avert judgmental bias
in resume screening. This review not only revealed several interesting insights but also showed that there is still much
to be discovered. Specifically, we discussed biased resume screening in the context of ethnic discrimination without
considering other stigmatising factors than ethnic makers (or intersectional effects). Further, we considered biased
resume screening from the HR professional/organisation perspective rather than the applicant/job seeker perspec-
tive. Applicants' perceived discrimination, however, may be as important as actual discrimination. Third, this review
focused on operational HR processes to manage organisational diversity rather than tactical and strategical HR pro-
cesses that also play a role (Shen et al., 2009). As we are among the first to summarise and integrate literature on
biased decision‐making in resume screening, still more aspects can be looked at to build an even more comprehensive
model. Finally, latest technology‐driven tools/systems (like algorithms and Open Badges) reflect not only the
changing nature of our labour market and talent acquisition/management in HRM but also the potential to counter
bias in early screening stages, if carefully thought through, developed, and implemented by HR professionals.
CONFLICT OF INTEREST
The authors declare that they have no conflict of interest.
Some disciplines (medicine, education, and academia) expect extensive curriculum vitae (CVs) that offer a complete career
history with detailed information on professional activities. This review focuses on resumes, which are a more abridged
career summary; however, much of the research reviewed may be applicable to CV screening.
Employment audit studies investigate labour market outcomes of applicants who are equally qualified for a job but differ in
nonjob‐related characteristics, like ethnic background. In correspondence audit studies, pairs of matched resumes are sent
to the same employer and the type and number of call‐backs are registered.
Implicit association tests are reaction time measures in which respondents are asked to match concepts (Arab‐sounding
names) to attributes (good/bad). The speed with which respondents do so is considered to reflect implicit attitudes
towards certain minorities.
Whitened resumes are ones where identifying information is concealed or blotted, for example, by using one's middle
name instead of first name if the former is more race‐neutral or by removing words referring to racial group membership
(like [Black] students' association).
eLancing websites are crowdsourcing internet marketplaces where employers place assignments (e.g., software develop-
ment and translations) that freelancers can bid for. Work is completed on an as‐needed basis and freelancers are
evaluated on the quality of their previous assignments.
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How to cite this article: Derous E, Ryan AM. When your resume is (not) turning you down: Modelling ethnic
bias in resume screening. Hum Resour Manag J. 2019;29:113–130. https://doi.org/10.1111/1748‐8583.12217
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