fewer connections than native job seekers for recommendation and referral
(Zhang and Chaintreau, 2021). Overall, her probability of successfully
reaching the end of a hiring pipeline has dropped substantially and more
sharply than for privileged groups. In addition, her migration background
makes JS less likely to gain support from social safety nets (Mar et al.,
2022; Che et al., 2020), increasing the urgency of her need and the unfairness
of the new status quo.

This section highlights two limitations of algorithmic fairness in hiring. On
the one hand, by restricting their scope to a single system at a precise point
in time, fairness measurements run the risk of missing the bigger picture,
i.e. the broader socio-technical system in which hiring algorithms are
embedded, which can change quickly and profoundly. A model deemed accurate and
fair in the old context may perform poorly in the new one. On the other hand,
these changes may be difficult to detect and quantify. Fairness evaluations by
practitioners on pre-deployment test sets, such as _Pymetrics Bias Group_
(Section 6.3), may quickly become obsolete. Strong shocks in the hiring domain
are an issue for data representativeness more broadly. Fresh data becomes
necessary. This is further complicated by changing incentive structures, by
the complexity of handling sensitive data, and by the frequent delay between
decisions and feedback in hiring.

##  9\. Opportunities and Limitations

Algorithmic hiring benefits from, and contributes to, fairness and anti-
discrimination work, as the previous sections have shown. In this section, we
summarize the emerging opportunities and limitations, from which we derive a
set of recommendations for researchers and practitioners, summarized in Table
6.

Table 6. Opportunities, limitations, and recommendations for algorithmic hiring and fairness research. |  Opportunities |  Limitations |  Recommendations
---|---|---|---
Bias & validity |  consider large candidate pools, reduce human biases, and attract minority candidates |  risk of encoding individual biases along with inevitable societal biases; invalid target variables |  focus on vulnerable populations beyond acceptance rates; study individual fairness and exploratory policies; scrutinize new tools
Broader context |  trigger positive feedback loops; consider tech-recruiter collaboration |  narrow focus on local outcomes can overlook fairness in entire hiring process; risk of repurposing for termination |  center on job seeker impacts; identify leaks in pipelines; design for recruiter-tech interaction; beware of performance and tenure prediction
Data |  support evaluations of diversity and inclusion |  reduced geographical, linguistic, and sensitive attribute coverage |  design data collection for diversity; develop IP-friendly audits
Law |  apply binding regulation to positively influence industry |  legal restrictions on fairness approaches: concerns about discrimination and data protection |  multidisciplinary research balancing fairness, privacy, and anti-discrimination; monitor EU AI Act

###  9.1. Bias and Validity

Opportunities. Algorithms for hiring can consider large pools of candidates,
avoiding the preliminary exclusion of unusual profiles, as often done by human
recruiters under time constraints. Under-representation and sampling biases
can be mitigated as a result. Algorithmic hiring also has the potential to
mitigate biases in imperfect human judgments. The simple fact of using
algorithmic decision-making can reduce avoidance by vulnerable candidates
(Avery et al., 2023). Fair and trustworthy algorithms can lead to a positive
form of automation bias and attract minority candidates.

Limitations. Data-driven algorithms tend to encode individual and societal
biases. Some algorithms are explicitly trained and evaluated to “predict the
competency scores candidates would have been given by trained human reviewers”
(ORCAA, 2020), inheriting individual biases from recruiters. Previous
experience is a preeminent feature for assessing candidates. In conjunction
with current job segregation, this means that the most important features are
inevitably skewed against historically disadvantaged groups. In addition to
these biases, the epistemic validity of prediction targets such as candidates’
_employability_ and _motivation_ is questionable. Job performance is famously
difficult to define and measure, let alone predict (Robotham and Jubb, 1996;
Wang et al., 2022). Algorithms cloaked in objectivity can promote bias while
targeting and legitimizing ill-defined quantities.

Recommendations. Attention should be devoted not only to acceptance rates for
vulnerable populations ($\operatorname{DI}$) but also to their representation
among applicants, as well as their progress post-hiring. For applications, job
descriptions and organizational communication can play an important role;
automation attempts (Qin et al., 2023) should carefully include fairness
evaluations. To mitigate biases against unusual candidates, individual
fairness and exploratory policies should be studied. Individual fairness
measures can surface problematic situations for individuals that may go
unnoticed when studying group fairness. Exploratory policies based on
partially stochastic mechanisms can provide new information in repeated
decision-making scenarios. However, the social acceptability and procedural
justice of such policies in the hiring domain remain to be studied. Finally,
new technologies, including AVIs and personality prediction, deserve
additional scrutiny, especially through the lens of validity theory (Rhea et
al., 2022b, a).

###  9.2. Broader Context

Opportunities. It is worth highlighting that improving fairness does not
require completely removing bias. Algorithmic hiring can reduce certain
disparities and trigger deflating feedback loops across bias conducive
factors. These algorithms do not (and should not) operate autonomously.
Effective and equitable hiring can result from a fruitful interaction between
technology and recruiters, leveraging complementary strengths. For example, HR
professionals are better suited to assess special cases and operate under
changing conditions (Li et al., 2021).

Limitations. Most fairness measures are focused on narrow algorithmic
outcomes, neglecting the wider socio-technical context around these
algorithms. Some of these measures are completely symmetric and consider
advantages for vulnerable and privileged groups as equally problematic (which
is, however, generally required by the law). Zooming out from single-algorithm
evaluations, it is worth noting that outcome fairness at every stage does not
guarantee fair outcomes for job seekers throughout the hiring pipeline.
Furthermore, fairness for employers is currently missing; two-sided platforms
would be well-positioned to study the performance of their algorithms across
employers, devoting special attention to small businesses. Finally, it is
worth noting that the discovery of patterns that predict job performance for
hiring can open the way to models for termination decisions.

Recommendations. We call for contextualized and integrated evaluations of
decision-making processes that go beyond the predictions of a single
algorithm. This will help to address complex problems, such as identifying
leaks in the hiring pipeline that are most critical for vulnerable groups and
modeling impacts on job seekers, such as their efforts and benefits. To
exemplify, rejected candidates may still benefit to some extent from a
specific type of explanation. Moreover, it will be important to better
understand the utility derived from these algorithms by different employers,
considering recruitment workflows and developing new fairness measures. The
prevalent _human vs. algorithm_ evaluation framework is of limited utility; to
overcome it, more research on recruiter-machine interaction is required (Sühr
et al., 2021), including candidate screening models (Alvarez and Ruggieri,
2023) leading to more granular measures. Finally, we invite special caution in
the development of predictive models for job performance and tenure, due to a
risk of exploitation for termination decisions (Zielinski, 2023); such an
application of algorithms raises even stronger ethical and social concerns,
which are only beginning to be discussed (Russon, 2020).

###  9.3. Data

Opportunities. Algorithmic fairness research is contributing additional
analysis into hiring practices from a perspective of diversity and non-
discrimination. More data entails more scrutiny and reflection, which can
inform organizational frameworks, such as _Diversity, equity, and inclusion_ ,
and scholarly fields, such as applied psychology and economics.

Limitations. Research on fairness in algorithmic hiring is based on data with
reduced geographical and linguistic coverage. In addition, important sensitive
attributes are missing from the data, making it difficult or impossible to
evaluate algorithms for specific vulnerable groups. Data and research are
constrained by a dual tension with the privacy of data subjects, on one side,
and the intellectual property (IP) of companies, on the other side.

Recommendations. Practitioners and researchers should seek more diverse data,
with greater geographical and linguistic diversity, and better coverage of
sensitive attributes that are relevant in hiring but are lacking, such as
disability status and sexual orientation. Dedicated initiatives should be
undertaken, including optional surveys for job applicants and broader data
donation campaigns (Bietz et al., 2019). Innovative auditing protocols should
be studied for employers and providers of algorithmic hiring solutions,
including IP-friendly data disclosure procedures.

###  9.4. Law

Opportunities. In practice, binding regulation shapes algorithmic development
more than ethical guidelines or self-regulation. Although clear guidance on
algorithmic hiring is currently missing, precise requirements set out in
future regulation and case law, informed by research and practice on fair
algorithmic hiring, have the potential to influence the industry positively
and profoundly.

Limitations. Most of the fairness approaches developed so far are restricted
to proxy reduction or removal, neglecting a wealth of solutions developed by
the algorithmic fairness community. This is most likely due to concerns of
infringing regulation on disparate treatment and direct discrimination.
Furthermore, special categories of personal data are lacking and difficult to
process under the current data protection law. Therefore, it is impossible to
assess hiring practices for vulnerable populations. Data protection law
restricts these analyses and should consider exceptions for algorithmic
fairness, as now suggested in the EU AI Act.

Recommendations. Algorithmic fairness can conflict with privacy and non-
discrimination doctrine. The exact contours of legally compliant algorithmic
fairness remain contested. The EU AI Act may offer a (limited) solution by
allowing certain types of sensitive data processing to remove biases in high-
risk scenarios. This guidance should be expanded to other areas and
jurisdictions. We advocate for further multidisciplinary research on this
topic, studying technical solutions and legal frameworks to reconcile these
principles in light of their trade-offs. Promising technological approaches
include multiparty computation (Kilbertus et al., 2018), sample-level
estimators (Fabris et al., 2023), and noise injection mechanisms (Juarez and
Korolova, 2023).

##  10\. Conclusions

The social, technological, and legal landscape around algorithmic hiring is
rapidly evolving; algorithmic fairness has become a necessary component for
both business-as-usual product development and frontier research.
Practitioners and researchers in this field must understand bias conducive
factors, leveraging contextualized measures carried out on appropriate data to
deploy suitable bias mitigation strategies. Multidisciplinary work at the
intersection with legal scholarship is especially critical to implement and
guide policy by defining technically achievable desiderata. Only a
contextualized and balanced understanding of fair algorithmic hiring can guide
research and practice to avoid the pitfalls of legitimizing questionable
applications with misguided analyses and to reap truly shared benefits for
society.

###### Acknowledgements.

We are indebted to many researchers and practitioners for advice on this work,
including Anisha Nadkarni, Anna Via, Carlos Castillo, Clara Rus, David Graus,
Didac Fortuny Almiñana, Feng Lu, Ilir Kola, Justine Devos, Marc Serra Vidal,
and Volodymyr Medentsiy. This work is supported by the FINDHR project, Horizon
Europe grant agreement ID: 101070212.

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##  Appendix A Systematic Review Methodology

This article is an interdisciplinary survey aimed at informing researchers and
practitioners interested in fairness and bias in algorithmic hiring. We
focused on a Computer Science (CS) perspective while summarizing key topics
from Human Resource Management, Industrial and Organizational Psychology,
Philosophy and Law with mixed methods, including the analysis of influential
technical reports, industry white papers and legal literature. Three sections
of this work, summarizing measures, mitigation strategies, and data (Sections
4-6), result from a systematic literature review summarized below.

  1. (1)

To ensure a broad coverage of the scientific literature centered on CS, we
leverage three scholarly search engines: IEEE Xplore, ACM Digital Library, and
Google Scholar.

  2. (2)

We use the query algorithmic $\wedge$ hiring $\wedge$ fairness on article
titles, where each term is expanded as follows:

     * •

algorithmic: algorithm* $\vee$ AI $\vee$ search* $\vee$ recommend* $\vee$
rank* $\vee$ screen* $\vee$ retriev*

     * •

hiring: hir* $\vee$ recruit* $\vee$ candidate* $\vee$ job* $\vee$ work* $\vee$
resum* $\vee$ CV $\vee$ interview* $\vee$ eval* $\vee$ appraisal. The last two
terms target the evaluation stage.

     * •

fairness: *bias* $\vee$ *ethic* $\vee$ *fair* $\vee$ discriminat* $\vee$
*equit* $\vee$ *equal* $\vee$ *parit* $\vee$ *symmetr* $\vee$ gap

  3. (3)

To ensure high recall, we consider the top 100 results. To guarantee
precision, we manually analyze each article and only select the ones that
treat fairness in algorithmic hiring from a quantitative perspective, i.e.
performing a fairness audit or introducing a novel method. To exemplify, we
discard articles on related yet different topics, such as freelancing (Hannak
et al., 2017), focused on human perceptions (Lavanchy et al., 2023), on
qualitative aspects (van den Broek et al., 2019), or mitigating biases to
improve accuracy without any fairness consideration (Chen et al., 2019).

  4. (4)

We take additional steps to further improve recall. For each included article,
we perform forward and backward snowballing (Wohlin, 2014), pre-filtering
article titles with a “hiring $\wedge$ fairness” query and applying the
inclusion criteria in (3). Finally, we consider articles and datasets
presented in related surveys (Fabris et al., 2022; Mashayekhi et al., 2022;
Zehlike et al., 2023) or published at dedicated venues,444Workshop on
Recommender Systems for Human Resources https://recsyshr.aau.dk/ finding one
additional dataset (IBM HR Analytics) and no additional article.