=Paper=
{{Paper
|id=Vol-3908/paper_19
|storemode=property
|title=Challenging "subgroup Fairness": Towards Intersectional Algorithmic Fairness Based
on Personas
|pdfUrl=https://ceur-ws.org/Vol-3908/paper_19.pdf
|volume=Vol-3908
|authors=Marie Decker,Laila Wegner,Carmen Leicht-Scholten
|dblpUrl=https://dblp.org/rec/conf/ewaf/DeckerWL24
}}
==Challenging "subgroup Fairness": Towards Intersectional Algorithmic Fairness Based
on Personas==
https://ceur-ws.org/Vol-3908/paper_19.pdf
Challenging “Subgroup Fairness”:
Towards Intersectional Algorithmic Fairness based on
Personas
Marie Christin Decker1, ∗ Laila Wegner1 and Carmen Leicht-Scholten1
1
RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
Abstract
To react to social injustices and harmful stereotypes caused by algorithms, fairness metrics evaluate how
well an algorithm performs for individuals or sets of groups. However, judging based on so-called protected
attributes squeezes people’s characteristics into fixed structures and ignores the socially constructed nature
of human identities. A more founded way to conceptualize the complexity of human identities demands to
engage in interdisciplinary theories from philosophy, sociology, and gender studies on intersectionality and
social identity. In this contribution, we sketch how current approaches to algorithmic fairness fall short in
considering human identities and intersectional realities. We propose an approach based on personas for a
more holistic view of humans and intersectionality in algorithmic fairness considerations.
Keywords 1
Machine Learning, Algorithmic Fairness, Intersectionality, Identity, Personas
1. Introduction
Given that algorithmic decision-making (ADM) can reinforce societal stereotypes and systematic
disadvantages, fairness metrics evaluate how well an algorithm performs for individuals (individual
fairness) or sets of groups (group fairness) (for discussion, see e.g., [1]). While computational fairness
approaches are valuable in a diagnostic function and draw attention to long-standing social injustices
[2], critical scholars emphasized several weaknesses of algorithmic fairness [3]. One point of criticism
is that the common algorithmic fairness metrics evaluate discrimination with respect to predefined
protected attributes as they are listed for example in the General Equal Treatment Act in Germany
(AGG) [4], the non-discrimination article by the European Commission [5] or Title VII of the Civil
Rights Act of 1964 in the U.S. [6]. These acts list explicit attributes such as Race, Color, Ethnicity,
Gender, Sex, Religion, (Dis)Ability, or Age based on which fairness metrics evaluate whether
differential treatment is present in an algorithm. This is criticized because people’s characteristics are
squeezed into and judged based on fixed attributes, ignoring the socially constructed nature of human
identities [7]2. A more founded way to conceptualize the complexity of human identities demands to
engage in interdisciplinary theories from philosophy, sociology, and gender studies on
intersectionality and social identity.
In this contribution, we sketch how current approaches to algorithmic fairness fall short in
considering human identities and intersectional realities. We propose an approach based on personas
EWAF'24: European Workshop on Algorithmic Fairness, July 01–03, 2024, Mainz, Germany
∗
Corresponding author.
marie.decker@gdi.rwth-aachen.de (M. Decker); laila.wegner@rwth-aachen.de (L. Wegner); carmen.leicht@gdi.rwth-
aachen.de (C. Leicht-Scholten)
0000-0002-9138-231X (M. Decker); 0000-0001-9983-8361 (L. Wegner); 0000-0003-2451-6629 (C. Leicht-Scholten)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
2
In addition to substantive criticism, there is also criticism of the term ‘protected attributes’ itself because it
evokes the impression that certain groups of people need (external) protection [8].
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
from human-computer interaction for a more holistic view of humans and intersectionality in
algorithmic fairness considerations that we plan to explore further in future research.
2. Forgotten Identities in Algorithmic Fairness
Concerns regarding algorithmic fairness are mostly grounded in the observation that algorithms
discriminate against certain attributes. However, Heinrichs [9] highlights that claims of wrongful
discrimination must be supported by ethical reasoning about what exactly makes it wrong. Evaluating
algorithmic discrimination and determining what makes an algorithm fair therefore also requires an
agreement on the underlying normative assumptions. To base this decision only on protected
attributes (as is often done in technical contexts) implies that a straightforward distinction between
unacceptable (= those based on protected attributes) and acceptable inequalities (= those not based
on protected attributes) is possible [10]. For example, discrimination based on math skills for a math
job seems intuitively legitimate. At the same time, most people would disagree that a feature such as
gender should influence the opportunity to work in a math job. Depending on what counts as morally
wrong, the list of protected attributes could be endless. While the law is certainly a first orientation,
critical scholars question whether it is fully suitable and sufficient for the context of algorithmic
discrimination [11].
The assumption that protected attributes can distinguish between wrongful and acceptable
discrimination is even more challenged by the socially constructed nature of categories [12]: concepts
such as gender or race are not solely descriptions of ones belonging to a fixed class, nor can they be
seen as natural or objective, but instead contain deeply inherited social values [13–16]. By
(computationally) reducing this ascribed social meaning to calculable categories, fluid concepts are
conceptualized as natural and objective facts [15, 17] – particularly neglecting the lived nature of
experiences that come along with belonging to more than one of these categories. Ignoring these
diverse experiences can reinforce stereotypes and stigmatization because it evokes the impression
that some group members (e.g., females) need protection and help in contrast to those more privileged
(e.g., males). Hoffmann highlights that a focus on discrimination and disadvantage additionally shifts
away discourses from the acquisition of privileges [11].
These challenges point already to the limited representation of identity in the context of using
protected attributes for algorithmic fairness. As highlighted by Crawford [18, p. 147], the
categorization of humans into fixed categories predefines the scope of the very dynamic and relational
nature of human identity and “restricts the range of how people are understood and can represent
themselves, and it narrows the horizon of recognizable identities.”. The consequence is a loss of
personal identification [14]: the observable characteristic ‘skin color’ does probably not match the
individual self-identification of a group which may be based on cultural traditions and the social
context [19]. A social category becomes a collective identity only if it is personally acknowledged – a
challenge when identities are summarized in fixed protected attributes. This is further underlined by
the Social Identity Theory coined by Tajfel [20] stressing the dynamic nature of human identity.
Briefly, the theory describes that people’s selves are made up of different identities. Identities are
significantly shaped by membership to one or multiple social group(s) – including the values and
norms that come along with these memberships. Only some of these identities relate to the protected
attributes defined by law [19, 21, 22], other groups include for example sports clubs or professions
[23].
That identities are complex and need to be considered in their interplay [24, 25] is particularly
highlighted in the notion of intersectionality coined by Crenshaw [26, 27]. In a nutshell,
intersectionality describes the overlap of systems of oppression, making people experience
discrimination on more than one level. The societal separation between privilege and disadvantage
follows individual attributes such as race, gender, or social class [28] which reflect patterns of status
and power and are turned into oppression (e.g., sexism, racism). Consequently, intersectional
discrimination cannot be considered the sum of multiple single discriminatory experiences [26, 27].
Intersectionality further highlights that those privileged determine the distribution of power.
Privileges refer to unearned advantages that people are entitled to because they belong to a certain
social group or have certain dimensions of their identity [29]. By exercising power, the privileged
group determines social norms, setting the normative ideal and creating a picture of the ‘others’ [29].
There have been approaches to account for intersectionality in the algorithmic fairness literature,
most of which compare parity notions of subgroups (e.g., Black women) [30]. Questions deal for
example with the correct number and type of subgroups. While these approaches are important to
make intersectional biases visible, an additional engagement with power dynamics inherent in social
categories is necessary to truly account for intersectionality in a sociological sense [11]. Creating
equalized subgroups may not emphasize that discriminations based on several attributes mutually
intensify each other and are not equal to the sum of individual experiences [13, 16]. Therefore, Kong
[30, p. 492] demands a shift of “the focus of fairness research from intersections of protected attributes
to intersections of structural oppression”.
While intersectionality is not directly analyzed with respect to the interplay of social identity,
critical scholars highlight the shortcomings in acknowledging the fluid and dynamic nature of human
identity in algorithmic fairness literature generally. For example, Lu et al. [31] argue that static, non-
relational categories of humans immobilize concepts of human identity and codify existing norms.
Motivated by a critical lens on protected attributes, Belitz et al. [32] develop context-specific
categories based on sociological approaches to identity. To do so, they incorporate self-identifications
into new classifications to bring the algorithmic representation closer to the self-concept of identity.
Considering self-identifications is an important step to include different social identities in
algorithmic fairness research. However, they derive categories from self-categorization which risks
becoming analyzed in isolation and thus may reproduce some of the shortcomings of protected
attributes. As we sketch in the following, our approach differs because we aim to consider a connected
abstraction of identity instead of categories by using personas.
3. Personas to Conceptualize Identity in Algorithmic Fairness
Building on the previous work on human identity in algorithmic fairness and informed by a critical
intersectional framework, we propose to target the coexistence of multiple social identities and its
consequences to conceptualize the whole complexity of algorithmic fairness. Therefore, we draw on
social identity theory and propose context-specific, participatively developed personas as an approach
to enhance intersectional algorithmic fairness. While personas were considered in [33], their
contribution focuses on AI in general and its implication for explainable AI. However, to the best of
our knowledge, the approach to include personas in algorithmic fairness research is novel.
Personas represent symbolic and fictive people vignettes that are developed to understand user-
specific requirements and interests, for example in human-computer interaction [34, 35]. To provide
an example, Marsden and Pröbster [34, p. 10] propose the persona ‘Lea’ for the design context of an
e-learning platform for women in tech: “We built a persona called “Lea” with a high career orientation
(I5), with a supportive husband who works parttime and two children. This persona is ambitious, very
organized, strategic, and interested in networking. She also networks in a very strategic way, reaching
out to people who can help her. Gender stereotypes annoy her and she cannot relate to any of them”.
Personas are used as representants for larger groups, combining individual attributes and social
categories. Unlike protected attributes, they target people’s goals, interests, and behavior for the
relevant context [36]. Therefore, personas should not be simplified by merging them into categories
like gender, race, or age, because this reproduces stereotypes and loses the diversity personas shall
bring [34]. As a connection between user and designer, they incentivize designers to take on the
perspective of different users in the design process [36–39] and to directly adapt the design to the
needs of the users [40].
In our research project, we wish to explore how personas can serve as an approach to integrate
the co-existence of multiple identities and thus, to represent the variety of human identity, into
algorithmic fairness research. We imagine the use of personas in all stages of the Machine Learning
cycle: For reflection in the design phase, for explicit consideration in the building phase as well as for
evaluation in the testing and monitoring phase. By using personas in these contexts, we anticipate
the following benefits: First, the use of personas is aimed to partly serve as an answer to the
weaknesses of protected attributes: Although personas are still an abstraction and simplification of
the reality, they do not replicate the separated analysis of individual attributes but represent a
combination of different identities. In this way, personas can depict intersectional experiences
contrary to the traditional abstractions in algorithmic fairness. By designing personas in a fluid
fashion, the binary and mutually exclusive narrative of protected attributes is aimed to be challenged
and individual experiences are included in the abstract representation of humans. Consequently,
intersectional personas represent different realities of life within legally protected groups and include
more social identities in their different contexts. Thus, personas are a step towards meaningfully
combining individual and group-based fairness metrics and can help to make these intra-group
differences more visible while still being generalizable. At best, a restricted focus on inter-group
comparisons which neglects differences within groups [41] might be avoided.
However, personas do not only combine different protected attributes. Instead, they create a
picture of imaginary real-life persons which “is meant to decrease our reliance on our own egocentric
perspective when reasoning about other people’s thoughts, feelings, and other subjective
experiences.” [34, p. 2]. This can be valuable to foster forward-looking responsibility in the context
of algorithmic fairness. Forward-looking responsibility describes the engagement with future events
and harm which shall be avoided in the first place. As discussed by Santoni de Sio and Mecacci [42,
p. 1067], algorithms introduce an active responsibility gap which creates an obstacle to forward-
looking responsibility. They stress that “engineers and other agents involved in the development and
use of technology may not be (fully) aware of their respective moral and social obligations towards
other agents”. Personas can add value by highlighting that algorithmic decision-making is not only a
neutral data collection but directly influences the reality of human beings. In this way, the humans
behind the data become visible and developers are reminded of the high stake and impact of their
decisions in the respective context.
Finally, meaningful personas might be used as an approach to challenge algorithmic power
hierarchies, which is one fundamental concern of intersectionality. To account for this, it is necessary
to question how – and by whom – identity is conceptualized, and what the underlying norms are.
Given the current power imbalances, a dominant group establishes societal norms around themselves
by defining other groups as the inferior deviation [43]. For the development of algorithmic systems,
this is especially relevant given current discussions around the formation of a coding elite [44]. This
coding elite is predominated by a homogenous and privileged group (western-centralized [15, 45–
47]), leading to the fact that their perspectives and experiences are built into the algorithms [15, 46,
47].
To avoid justice evaluations without considering those affected, it is necessary to approach these
questions from a bottom-up perspective [48]. Personas might become a means to represent the variety
of human experiences if they are developed in cooperation with those affected, for example, based on
participatory approaches. Participatory personas ought to include marginalized perspectives so that
they become a representative to give everyone a voice – at least in the sense that everyone in the
given context can self-identify broadly with one of the developed personas. By valuing lived
experiences, meaningful personas may become a means to reduce relational and epistemic injustices
and acknowledge power hierarchies. This means also including personas that represent rather
privileged identities, to overcome the single-axis focus on discrimination in algorithmic fairness
research, as demanded by [11]. Thus, personas can help to question privileges instead of taking them
for granted and only highlighting disadvantages. Engaging with privileges is an important part of
embracing forward-looking responsibility as demanded above3.
Constructing meaningful personas is not without challenges. Personas need to simplify and
abstract complex social realities without risking reproducing stereotypes or creating a false sense of
understanding [34, 50]. Further, personas depend heavily on the context of use. For example, people
can have different attitudes and experiences in their job and private life. This makes personas
resource-intensive and costly to construct, especially when using qualitative or participatory
approaches.
However, we are convinced that future research in algorithmic fairness should engage with
overcoming the sketched oversimplified assumptions of human identity that create fixed norms and
restrict humans in their diversity. Further, future research should engage with approaches to
intersectionality that can account for power hierarchies without rebuilding assumptions of privileges
into fairness evaluation, mirroring stigmatizing group hierarchies. In this short contribution, we
proposed to use personas to account for these challenges and motivate our research proposal by the
outlined advantages that come into play when limited protected attributes are replaced by meaningful
personas. In future work, we plan on designing participative personas and test different scenarios on
how to integrate them in the Machine Learning cycle.
Acknowledgments
This research was conducted within the RRI Hub at RWTH Aachen University. The RRI Hub is funded
by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) as part of Germany’s
Excellence Strategy.
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