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Computer Science Communities: Who is Speaking, and Who is
Listening to the Women?
Using an Ethics of Care to Promote Diverse Voices
Marc Cheong, Kobi Leins, Simon Coghlan
Centre for AI and Digital Ethics (CAIDE), Faculty of Engineering and IT (FEIT)
University of Melbourne
Parkville VIC Australia
{marc.cheong, kleins, simon.coghlan} @ unimelb.edu.au
ABSTRACT
Those working on policy, digital ethics and governance often refer
to issues in ‘computer science’, that includes, but is not limited to,
common subfields of Artificial Intelligence (AI), Computer
Science (CS) Computer Security (InfoSec), Computer Vision (CV),
Human Computer Interaction (HCI), Information Systems, (IS),
Machine Learning (ML), Natural Language Processing (NLP) and
Systems Architecture. Within this framework, this paper is a
preliminary exploration of two hypotheses, namely 1) Each
community has differing inclusion of minoritised groups (using
women as our test case); and 2) Even where women exist in a
community, they are not published representatively. Using data
from 20,000 research records, totalling 503,318 names, preliminary
data supported our hypothesis. We argue that ACM has an ethical
duty of care to its community to increase these ratios, and to hold
individual computing communities to account in order to do so, by
providing incentives and a regular reporting system, in order to
uphold its own Code.
KEYWORDS
Gender, Diversity, Computer Science, publishing, research, sex
equality, gender representation.
1 Introduction
Lack of diversity in research and development of computing
technologies has long been a well-recognised problem [10,16,47].
This persisting problem concerns both questions of fairness and the
quality and breadth of research. Homogeneity in computer science
communities is the most the recent target of a slew of research. [15,
52] Some argue that decolonisation of computing and big data –
which is amplified at speeds and scales previously unimaginable –
needs to be given much greater regard within computational
communities [5,33]. Although the sciences are often thought to be
neutral and unbiased, the lack of diversity and of a variety of
different voices in scientific fields is increasingly being researched
— not just for the sake of individuals but also in the interests of
maximising the value and quality of research in those fields. The
term minoritised, rather than minority, is used because those
represented are not necessarily in the minority more widely
speaking, but rather are effectively kept in the minority for a wide
range of reasons, usually by a dominant group [12]. To explore
these issues, we turn to the ‘ethics of care’, in part because of its
emphasis on closely attending to the experiences and needs of the
minoritized. Using this care ethics approach, we argue that there is
an duty of care and justice to increase diversity within computer
science communities, and that those with the power need to act to
include these people and voices in computational sciences. In this
paper, we focus on women in computer science. However, we also
suggest that our broad findings about duties of care and justice can
be applied to other minoritized groups. We also argue that, in
addition to meeting ethical responsibilities, creating more equitable
representation can strengthen the disciplines themselves.
Lack of diversity can affect both natural sciences and the
humanities. For example, a study on research into birdsong
unearthed data that showed that research had been skewed towards
male birdsong until female researchers focuses also on female birds
[19]. Similarly, in philosophy, “feminist methods of articulating
ethical theories” in areas such as moral philosophy have been few
and far between until the emergence of feminist ethics in the 1970s
WORKING DRAFT COPY ONLY.
Please cite final version.
Final version published as:
Marc Cheong, Kobi Leins, and Simon Coghlan. 2021. Computer
Science Communities : Who is Speaking, and Who is Listening to the
Women? Usin g an Ethics of Care to Promote Diverse Voices . In ACM
Conference on Fairness, Accountability, and Transparency (FAccT
’21), March 3–10, 2021. Virtual Event, Canada.ACM, New York, NY,
USA, 10 pages. https://doi.org/10.1145/3442188.3445874
DRAFT COPY ONLY. PLEASE CITE FINAL VERSION.
and 1980s [40]. In tertiary education, female academics are often
judged much more harshly by male and female students alike.
[6,30,31,34,43]. Although this question is relevant for a much
broader group of non-mainstream computer scientists, gender is the
easiest (within a margin of error) to identify and map empirically.
For this reason, we have started the discourse in an area where we
could perform automated quantitative analysis. This paves the way
for future research involving, for example, interviewing experts
within each of the sub-fields to gain a clearer understanding of their
cultures and drivers.
In computer science, the statistics
1
are not encouraging: the
“number of women studying computer science is falling… since
the 1970s” [35], with numbers “… falling pretty steadily since the
80s, despite the increase in demand for these types of skills” [35].
In the broader picture, “women in STEM [Science, Technology,
Engineering, and Mathematics] make $16,000 less on average than
their male counterparts” [35]. To date, the only breakthroughs
include one recent paper has been written about the role that gender
plays in senior roles and publishing within HCI [32].
However, there is a gap in extant research on the status of female
academics across computer science communities which look at
measures of ‘impact’ in the context of citations of ranked
conferences and papers. Although there are many ways to assess
diversity, such as comparing the number of employees in a field or
the number of graduates at a University, we choose to take the
approach of using publication rates. Given the number of fields we
are analysing, and the volume of the data available, we chose this
last approach as an entry point to start investigating the state of the
different computer science fields relative to each other.
We wish to stress upfront that gender identification is problematic
and may cause representational harm, and that sexuality is non-
binary, an issue surveyed in some detail by Saif M Mohammad
[37].
There are broad methodological caveats that we would like to
address upfront in the spirit of transparency:
1. Extant algorithms assume gender is a binary construct,
and hence do not account for diverse gender identities. Reiterating
a point in [37]: “Gender is complex, and does not necessarily fall
into binary male or female categories (e.g. nonbinary people), and
also does not necessarily correspond to one’s assigned gender at
birth” [37].
2. Most systems for gender determination are deterministic
based on prior statistics of name usage. Hence, there is no other
1
Besides having a gender disparity, computer science still has a disparity in enrolments
based on ethnic background: an average of $14,000 [35] difference between White
Americans and Black/Hispanic Americans in STEM fields.
2
See also Catherine Connell. 2010. Doing, Undoing, or Redoing Gender?: Learning
from the Workplace Experiences of Tran s people. Gend. Soc. 24, 1 (Feb. 2010), 31–
55;
context [36] – e.g. an individual’s confirmation of preferred gender
marker – beyond the mere isolated first name. Such binary gender
research omits many minoritised groups. A “strong normative
tendency to use names to signal gender” can also lead to
misgendering – “a machine associat[ing] someone with a gender
with which they do not identify” – which causes harm [37]
2
.
3. Statistics on first names are biased towards
popular/frequently-occurring names in a mostly anglophone
context, and are limited to the cultural and temporal context when
the list is produced. (e.g. the USA Social Security Administration
Baby Names dataset).
We note that although we are focusing on women, our concerns
include broader intersectionality and representation within the
field. We are also concerned about marginalisation of all those
whose voices need to be amplified in the various fields of computer
science. We have chosen to use the blunt instrument of automated
identification of gender based on names not because it is without
risk, but because it is critical that we ensure diversity to ensure more
fair, accountable and transparent systems, and this is one method to
provoke that debate. This is not a study of individuals, but an
attempt to provoke discussion on the basis of a broad stroke
analysis that, although problematic, we suggest is useful to move
the conversation, and ethics of care and justice, forward to ensure
inclusive and better research in computer science. Hence, in this
paper, we seek to answer these four initial research questions.
RQ1. How are female academics currently represented in
publishing in each of the subfields of computer science?
RQ2. How do the statistics of publications compare to female
representation in each computing science community, based on
existing research? What differences, if any, are there between
different computer science communities?
RQ3. What might ethics, and specifically an ethics of care, have to
say about underrepresentation of women in computer sciences?
RQ4. How can we reduce the disparity?
We organise this paper as follows. First, the literature of gender
bias in computer science and associated fields are surveyed to
ascertain the current state of gender diversity. Second, we detail our
experimental methodology: the choice of subfields surveyed in our
experiment; our data sources and data analysis choices; and ethical
considerations. Third, we analyse our results based on extant
surveys of subfields, and those found in our initial quantitative and
qualitative research. Fourth, we introduce an “ethics of care”
also:
Foad Hamidi, Morgan Klaus Scheuerman, and Stacy M Branham. 2018. Gender
Recogn ition o r Gender Re duction ism? Th e Soc ial Implicatio ns of Embe dded Gender
Recogn ition System s. In Proceedings of the 2018 CHI Conference on Human Factors
in Computing Systems (Montreal QC, Canada)(CHI ’1 8,Paper 8) . Association for
Computin g Mach inery, New York, N Y, USA, 1 –13
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framework, and explore some of its implications in computing
sciences. Finally, we introduce some recommendations and explain
what further research is necessary.
2 Literature Review
Some research documents the gender disparity in the sciences
including the computer sciences. A 2001 qualitative study at
Stanford University found women only made up 9.75% of
Computer Science professors [2]. Causes identified by the
researchers include “motivation, parental support, balancing family
and personal life, any perceived gender biases or discrimination
against women, the enticement of industry versus academia, and
the views of both women and men towards women in computer
science” [2]. In a recent study on academic publishing statistics
across “83 countries and 13 disciplines” [25], Huang et al
investigated citation data of 1.5 million authors, with their gender
identified from academic authorship records on Web of Science,
and independently replicating the results against the Microsoft
Academic and DBLP databases. Their findings indicate that
“…paradoxically, the increase in the number of women academics
over the past 60 years has increased … gender differences in the
total productivity and impact of academic careers” [25] in STEM.
This matches findings in other studies [35].
Extrapolating to future authorship trends in computer science,
Wang et al [61] conducted an analysis on 2.87 million computer
science academic publications since the 1970s, using name-based
gender inference and time series forecasting. The forecast is rather
grim: “based on recent [gender] trends, the proportion of female
authors in Computer Science is forecast to not reach parity in this
century” [61]. This result agrees with the work of Holman et al,
whose analysis of “36 million authors from >100 countries
publishing in >6000 journals” [24] reveals a gender gap which is
“likely to persist for generations… [and] clearly require[s]
additional interventions if parity is to be reached this century” [24].
It should be noted that other dimensions – in particular local
cultural and political context – can have an intersectional [33]
negative effect on diversity. A study by Thelwall et al in the context
of STEM in India, found that overall there is a “…substantial
overall male bias, [but the] broad research field choice is less
influenced by gender” [56]. In other words, male bias still exists
overall, but the distribution of bias across subfields are different.
Local factors play a role in changing this distribution: examples
include a higher coverage of “algorithms in Indian mathematics
[studies]…” and “a tendency for males to research thing-oriented
topics and for females to research helping people and some life
science topics” [56]. A 2008 qualitative study by Lagesen
conducted in a Malaysian computer science faculty revealed that
more than half the Bachelor students in computer
science/information technology are female, around 40% of
postgraduates are female, and encouragingly “the majority of the
faculty, as well as all heads of departments and the dean, were
women” [28]. It is clear that computer science is not inherently
gendered – women can and should succeed given the right
circumstances, which may include responsible action by the
profession.
Many of the above studies refer to a ‘pipeline effect’ [2,28] – i.e. to
the likelihood of leaving academia as one progresses through the
academic ‘pipeline’ from undergraduate years to tenure (see also
[49]). There is also literature on the motivations and experiences of
university undergraduates in pursuing (or otherwise) computer
science-related courses. Sax et al.’s review of American university
students’ survey responses in their freshmen year (sampled from
1976 to 2011) found a “persistent, sizeable underrepresentation of
women across all years” [46] in computer science. At the same
time, the number of women in STEM and other professions more
broadly has risen [38].
3 Methodology
The motivation for this research was to analyse publication rates by
gender in individual communities within computer science, from
1969 to 2020 (inclusive). The reason is that much of the research
on governance and ethical considerations regards computer science
as a single area of research, when, in fact, it is constituted by many
individual (sometimes overlapping) communities.
In designing this paper’s methodology, we consulted existing
literature on experimental methods or heuristics used to
approximate gender representation in academia. The most
frequently used heuristic is inferring gender from author names in
publication records, accessible via academic citation databases
[24,25,56,61]. Commonly used databases include DBLP, Microsoft
Academic, Scopus, and Web of Science. In terms of the actual
algorithmic methods used for name-to-gender inference,
Santamaría and Mihaljević [45] have conducted a thorough
literature review and benchmark on five such services. In brief,
there are both offline techniques using open data (such as gender-
guesser [42] for Python which is based on curated data sources
[45]), and online techniques (web services or APIs which are
proprietary in nature, such as genderize.io and GenderAPI). Offline
techniques use simple statistics based on frequency of names, with
the advantage of transparency and simplicity [9] but with the
disadvantage of not being frequently updated or representative of
the global population (e.g. not being able to infer culturally-diverse
names). Online techniques are the inverse: they have a higher
accuracy and inclusion of diverse names, but with the disadvantage
of being a commercial offering without much transparency about
their inner workings. The use of name-to-gender algorithms have
ethical caveats and limitations, documented per our Introduction in
Section 1.
3.1 Determining Subfields of Study
For this paper, we have identified nine subfields of computer
science, based both on our own experience in the discipline and the
identification of common research focus areas in top universities.
These subfields represent common research themes in academic
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institutions with a computer science department (which included
similar-sounding departments such as computer systems and
information technology). These nine subfields are: Artificial
Intelligence (AI), Computer Science (CS), Computer Security (or
cybersecurity or information security, abbreviated InfoSec),
Computer Vision (CV), Human Computer Interaction (HCI),
Information Systems (IS), Machine Learning (ML), Natural
Language Processing (NLP), and Systems Architecture (SA). The
capitalised proper nouns are used to uniquely identify this subfield
as indicated in Microsoft Academic (Section 3.2), and the
abbreviations are used throughout this paper for brevity. To
elaborate, CS, when referred to as a subfield, concerns areas such
as theoretical computer science (e.g. formal methods). ACM is the
world's largest educational and scientific computing society and
delivers resources that advance computing as a science and a
profession, including to each of these communities.
However, we note that an important area of emerging research in
computing – Information Ethics (also known as AI Ethics and
equivalent) – is not covered in our survey; nor is the long-
established area of Software Engineering. The latter is, for our
purposes, classed as a subfield of engineering. And although there
have over many decades been nods to ethics, the field of ethics in
computer science is still emerging as a truly interdisciplinary field
and as a rigorous discipline that, for example, involves experts in
philosophical ethics .
3.2 Data Collection
Based on the literature surveyed e.g. [25], we have decided to use
Microsoft Academic [51] as our data source, as it comes with a
programmer-friendly API
3
for programmatic data downloads of
citation information. More importantly, Microsoft Academic also
provides author and paper metadata, such as research organization
and paper category (topic), which lets us classify each paper based
on their subfield of computer science (Section 3.1).
For each of the 9 subfields in Section 3.1, we query the Microsoft
Academic API v1.0 [51] for 20,000 citation records, provided on a
best-effort basis using the default parameters of the API. The total
of N=20,000 is chosen as it balances the need for a large sample
size with due consideration for Microsoft’s server resources.
Experimentally, values higher than 20,000 results in server time-
outs which indicate a high server load; we avoid this to conserve
server resources. To further reduce the strain on the server in
consideration of the data provider and other fellow users, successful
data fetches are limited to no more than two per hour, and the
metadata items requested are limited to only a subset of the full
metadata available.
The results provided from the Microsoft Academic API are in JSON
format, which is then processed in Python for subsequent steps. We
3
We initially considered the use of the ArXiV repository as it is a popular site hosting
preprints for computer science papers. Unfortunately, author first names are provided
only as initials (e.g. ‘J. Doe’ instead of ‘Jane Doe’), which render the name-to-gender
algorithms ineffective.
firstly perform data deduplication by removing any duplicated
citation within- and across-categories, such that any unique citation
appears only once within the entire dataset. (Ties are broken in
alphabetical order, e.g. a paper which has been dual-classified in the
topic ‘Artificial Intelligence’ and ‘Machine Learning’ will be
included in the former, but not the latter). A grand total of 150,651
citations are obtained after the deduplication process.
3.3 Gender Inference: Technology, Caveats,
Considerations
By considering the options in our aforementioned literature review
on name-to-gender algorithms [45], we have decided to use a two-
step process in the interest of reducing costs (in the case of paid
online services), while maintaining some degree of transparency to
the process (by prioritizing offline methods using published
datasets).
Based on the analysis given in [45] as well as initial
experimentation with popular Python gender-detection libraries
4
,
we have chosen gender-guesser for the offline option; and keeping
in line with extant research methods [25,50], we chose the
genderize.io web service as the paid online option.
To recap, Section 1 covered broad methodological caveats that we
would like to address upfront.
The following algorithm was used in determining gender
distributions in each of the particular CS subfields.
1. For each citation, obtain first names of all authors.
2. Authors whose first name consists of a sole initial are
discarded as the gender detection algorithm
5
will not
work.
3. Each author’s first name is first processed with the offline
gender-guesser [42] Python library, which returns a
classification and a classification confidence:
{‘male’, ‘mostly male’} (i.e. predicted as male with a
higher and a lower degree of confidence respectively),
{‘female’, ‘mostly female’} (as before),
‘androgynous’ (gender neutral), or ‘unknown’.
The difference between the last two is that androgynous
names could statistically be in either the male and female
4
Some libraries including gender-detecto r w ere promising can didates due to their
usage of open datasets, but ultimately were not suitable for our purposes due to
performance issues and compatibility issues.
5
See footnote #3.
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name classes, e.g. Pauley [42]; whereas unknown names
are not found at all in the dataset used by the library.
4. Any names marked ‘unknown’ by gender-guesser are
likely to be from a non-anglophone background: hence,
based on the track record of genderize.io for processing
culturally-diverse names, it is used for second-round
processing. Genderize.io returns either ‘male’, ‘female’,
or ‘unknown’.
5. The final classification from Step 3 (or Step 4 if Step 3
was inconclusive) was then was then used an output point,
in the overall aggregation. Repeat Step 1 for all authors
per citation, and for all citations in dataset.
Note that we do not remove duplicated names across papers – e.g.
if hypothetical author Shanti Kumar was present across three papers,
we count three separate instances. This presupposes that removing
duplications may in fact present a more negative outcome than the
current analysis (containing a margin of error) already present;
further, we do not wish to dilute the contribution of a single author
but instead would want to consider overall impact.
4 Experimental Results
4.1 Statistics on Gender in Subfield
Figure 1 illustrates the overall distribution of the genders resulting
from our inference method (Section 3.3): the left inset includes
names where gender cannot be inferred reliably by automated
techniques (undetected), as well as when a name is deemed to be
gender-neutral; and the right inset has gender-neutral terms and
undetected names removed, for a direct comparison. The total
number of author names range from 42,991 to 65,979
(mean = 55924.22, s.d. = 7737.88) per subfield.
The proportion of males outnumber those of females for each of the
nine subfields in our study. For the direct comparison case, a simple
Analysis of Variance (ANOVA in Microsoft Excel’s Data Analysis
Toolpak) with alpha = 0.05 confirms that the count of males versus
females is statistically significant.
(F value = 214.63) > (F critical = 4.49), with
p = 1.08774E-10.
4.2 Gender Diversity by Subfield
4.3.1 Less Gender Diverse: AI, CS, InfoSec, CV, ML, SA
From Figure 1(b), we observe that gender representation in
publications within a particular subfield is roughly divided into two
categories, based on ratio of gender, which we term ‘less gender
diverse’ and ‘slight improvement’.
The former is the subject of analysis in this subsection. Accounting
for a direct male-to-female comparison, these subfields of AI, CS,
InfoSec, CV, ML, and SA have between 10% to 20% female
authorship. This translates to an approximate 5:1 ratio of males to
females. To make sense of these statistics, we turn to extant
literature discussing the state of these subfields, to compare our
findings against the actual population of female academics.
Figure 1. (Left) Gender distribution of authors by subfield, including gender-neutral names and undetected names;
and (Right) Direct comparison between male and female author ratios, excluding gender-neutral and undetected names.
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AI and ML has been traditionally low in female representation,
with “significant differences in machine learning and computer
ethics between the United States and the United Kingdom as well
as differences in the research focus of papers with female co-
authors” [54]. One hypothesis is that subfields with high emphasis
on mathematical and scientific techniques (such as ML, CV) suffer
more from historical biases, leading to an overrepresentation of
males (see Section 5.2). When we examine InfoSec, industry trends
in 2016 based on a survey by the ISC2 cybersecurity professional
organisation
8
are such that “…women in the information security
profession represent 10% of the global workforce, a percentage that
remains unchanged from the 2013 study [but]… 26% of IT
professionals worldwide are women” [11].
4.3.2 Slight Improvement: NLP, IS, HCI
The second category encompasses the subfields of NLP, IS, and
HCI, and has the proportion of female authors ranging from 20% to
30%. These have a nett effect of a roughly 3:1 male:female ratio.
Extant findings from these subfields explain the slight
improvement in the male:female ratios. HCI has had issues
regarding representation of women which are canvassed by McKay
and Buchanan [32]. Via an analysis of OzCHI, the Australian HCI
conference venue, these authors claim that “…female
representation is quite good, but we need to be cautious to preserve
it”. In the subfield of NLP, we hypothesise that female
representation is higher than the broader umbrella of AI, based on
the anecdotal evidence from research addressing language bias [55].
To quote Leavy, “…[l]eading thinkers in the emerging field
addressing bias in artificial intelligence [specifically language
models]... are also primarily female, suggesting that those who are
potentially affected by bias are more likely to see, understand and
attempt to resolve it” [29].
As for IS, academics in this subfield are in a position to “contribute
to addressing the challenge of gender imbalance in the IT
profession” [18], noting that issues of gender discrimination in IS
have been found as early as 1996, if not earlier [60]. A more
detailed discussion on female perceptions of IS and subfields which
closely relate to the genesis of computing can be found in Section
5.2.
4.3 Algorithmic Limitations
Our study has limitations. The automated nature of gender
recognition has technical and methodological caveats as detailed in
Section 3.3.. From initial experiments, the margin of ‘undetected’
names is unacceptably high (ranging from 19.40% to 31.07%) in
the absence of using the Genderize.io as a second step. This
illustrates the fact that existing methods still have a way to go in
future research. Human judgement remains preferable over
machine analyses and could be performed in future research with
8
The organisation conducting this survey is stylised (ISC)².
smaller samples, or in a hybrid human coding technique assisted by
a rule-based system.
5 Discussion
5.1 Lessons from the Ethics of Care
Our results raise important ethical and social issues. To explore
them, we shall use the normative theory known as “ethics of care”
or “care ethics” (CE) [22,53]. We choose this ethical approach not
because it is the only useful normative theory for exploring such
issues, nor because it is beyond criticism (all normative theories are
controversial), but because its strong connection to key themes
arising from issues pertinent to our study makes it particularly
illuminating. Although CE has been applied in some detail to areas
like medicine [57] and business [21], it has been less frequently
applied to computer science [21,50].
CE arose in the 1970s and 1980s in the context of feminist critiques
of male-dominated historical and prevailing ways of doing
philosophical ethics. Although CE and feminist ethics [26,58] are
distinct—such that, for example, one can be a feminist ethicist
without subscribing to CE—CE nevertheless draws heavily on
feminist modes of thought that question ‘masculine’ moral
approaches and assumptions that historically were never or were
only rarely questioned. CE criticised the then dominant conception
of the moral agent as independent, unattached, self-sufficient,
unemotional, and rationalistic.
Psychologist Carol Gilligan’s seminal early 1980s work In A
Different Voice brought to light the dominance in ethics discourse
of moral values related to this conception of moral agents [17].
These associated values included a preference for relatively
unvarying principles and rules, impartiality and detachment, liberal
ideals of justice, and contractarian thinking. At around the same
time, the philosopher Nel Noddings [39] emphasised the
importance to moral agency and experience of human
interdependence and of caring and being cared for. The emerging,
more relational conception of moral agency and moral life stressed
values such as responsiveness, compassion, contextual
understanding, and co-dependent relationships. A greate r
attentiveness to lived experience—as opposed to a detached
manner of observation that obscures the details of individuals’
lives—was brought to the fore.
Since Gilligan and Noddings, a range of female (and sometimes
male, [53]) philosophers have added to the corpus of work on CE,
deepening its basis and applying its ideas to a range of contexts and
practices. Philosopher Raja Halwani summarises the essence of CE
by identifying four of its ‘desiderata’, namely:
…the concern with people embedded in contextual relations;
attention to areas of life, neglected by some traditional moral
philosophy, such as friendship and the family…; the emphasis
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on the emotive component in ethical engagement; and partiality
[20].
Thus, CE foregrounds nurturing relationships, biological and
emotional needs, affective engagement or what exponents call
“engrossment” [38], and the role of associated contextual features
in shaping morality and giving rise to responsibility and obligation.
For many care ethicists, these elements spring especially from the
lives and historical experiences of women; such so-called
“feminine” values and experiences tend to stand in contrast to the
experiences of men and the values championed by predominantly
male philosophers. In these ways, CE represents a distinct approach
to ethics alongside the more established normative theories of
deontology, utilitarianism, contract theory, and (to an arguably
lesser extent) virtue ethics. However, CE has been criticised on a
number of fronts. Broaching several such criticisms, and briefly
discussing how CE can respond to them, will help us to apply this
approach to the findings of this study.
CE might be criticised for essentializing gender, overlooking
gender diversity and fluidity, and stereotyping men and women.
For example, some men embody ‘feminine’ values like nurturing
and compassion just as some women embody ‘masculine’ values
like moral rationalism and extreme impartiality. However, although
CE grew from and encompasses feminist critiques of male-centred
ethics, it may reject essentialism, accept non-binary views of
gender, and agree that ‘feminine’ values of caring are not the
exclusive preserve of women. Indeed, it may champion these
positions while stressing that women nonetheless are often well-
placed to identify and respond to the values at the heart of the CE
approach. In Noddings’ words, there are “centuries of experience
more typical of women than men” [39]. Such experience, of course,
includes not only the experience of the mother-child relation, but
the experience of caring more generally [44].
The patriarchal conditions of women’s historical caring (including
motherhood) may raise the concern that the caring outlook is not
always laudable but rather may be a function of oppression and an
associated distortion of perspective. Entrenched power imbalances,
it might be suggested, could have led to forms of moral blindness
centring on a problematic valourising of caring relations [8]. Yet a
CE proponent may reply that these historical and, moreover,
persisting inequalities in power may, on the contrary, often give
women—and, we should also stress, women at various
intersections, such as women of colour and women with
disabilities—greater insight into a range of moral matters,
including the unfairness of many circumstances and caring
relations occupied predominantly by women, the oppression of
people of different sexualities and genders, and the needs of
individuals and groups who are marginalised and especially
vulnerable. This is not to say that men cannot also adopt such
perspectives and the relevant forms of moral attentiveness; it is
9
A particularly relevant quote by Steve Henn explains this: “A lot of computing
pioneers — the p eople who programmed the first digital computers — were wo men.
And for decades, the number of women studying computer science was growing faster
rather to note that ethical insight into certain states of affairs can
sometimes be sharpened by cultural, historical, and biological
circumstances. At the same time, we can say that from the point of
view of care ethicists, men too have reason to adopt a CE position.
Given these points, it is perhaps somewhat ironic that CE has been
criticised for lacking the resources to give guidance on political and
moral questions regarding those to whom we do not have special
and partial relations. Nonetheless, it is a serious question. Can CE,
then, say anything significant about justice outside of those
paradigmatic relations of care? [13,59] There are many individuals
more distant from us both geographically and personally who are
nevertheless particularly vulnerable. CE, its proponents may say,
can both recognise and highlight the situations of such people and
the moral necessity for them to receive care just as those personally
close to us need and deserve care. Furthermore, the ethical
attentiveness and responsiveness to need that is rightly cultivated
and honoured in more personal caring contexts can in some form
be extended to strangers, be they disenfranchised fellow citizens or
people from foreign places. The caring attitude can also be
extended to, say, academic colleagues who have needs and require
support and even sometimes nurturing. Therefore, CE need not
displace or overlook the promotion of justice for more ‘distant’
others, but rather can (arguably) recognise, inform, and deepen the
notion of justice. Again, people of any sex or gender can adopt this
CE viewpoint.
5.2 Ethics of Care and Computer Science
Having briefly outlined some of the features, problems, and
strengths of CE, we can now apply it to our findings. Our study
suggests that women are strongly underrepresented in all subfields
of computer sciences, in some even more than in others. From a CE
perspective, this is problematic for several reasons which we will
now briefly discuss. In the first place, there is the issue of justice or
fairness for women themselves. Many women have historically had
strong interests in pursuing careers across the various divisions of
computer science. However, due to historical and persisting power
imbalances which may include both overt discrimination and subtle
and implicit biases, it is often harder for women than it is for men
to enter computer sciences and, once there, to climb institutional
ladders. Interestingly, it was not always this way. The first
analytical machine, the precursor to the computer, was created by
Ada Lovelace [48]. Computing was predominantly populated by
women until it became a ’profession’ and was paid a proper salary
- around the mid-1980's
9
[23].
In essence, critical factors dissuading participation of females in
computer science in the beginning of the ‘pipeline’ include the
change in perception from the 1970s, from a field perceived to be
“… more clerical in nature” to its redefinition as “a science…
than the number of men. But in 1984, something changed” [23]. See also
https://www.history.com/news/coding-used-to-be-a-womans-job-so-it-was-paid-less-
and-undervalued
DRAFT COPY ONLY. PLEASE CITE FINAL VERSION.
[which] distanced itself from skill sets traditionally thought to be
well suited to women and sought to align itself with other science
fields like engineering that had strong masculine connotations” [46]
(citing [14]). Existing biases against women in STEM-based
academic jobs may draw them towards leaving the ‘academic
pipeline’ [2] (see also Section 2). This effect is partially offset by
countervailing factors such as the promotion of computing skills in
mathematics [56] or affirmative action policies to close the gender
gap [7] .
Nonetheless, as our results show, it appears to be the case that
women are particularly excluded from certain areas of computer
sciences like CV, AI, and InfoSec. Moral attentiveness and
responsiveness to women’s important needs are qualities and
behaviours that a CE viewpoint can endorse and promote. In
addition, CE can, as we explained, recognise the importance of
forms of justice that regard others as deserving of care, and, in this
specific context, as individuals whose talents and aspirations
should be appreciated and nurtured. As we noted, CE is strongly
oriented to recognising and responding to historical and persisting
disadvantage and vulnerability.
Furthermore, CE suggests that, again due to historical and
persisting circumstances, women, including those with lived
experience at various social and political ‘intersections’,often have
particularly keen moral insights into matters related to care, gender,
vulnerability, marginalization, and so on. Again, making this claim
is not necessarily to fall prey to gender essentialism or the
stereotyping of any particular gender; it is rather to register the
potential effects of context and circumstance on the moral
attentiveness and responsiveness of certain situated individuals.
Insofar as (some) women bring these qualities and perspectives into
areas of computer science, they may help to expand the moral
awareness of their colleagues, including those who are more
established and in positions of power. And this may be of benefit
to future women and other marginalised groups who want to work
in those fields.
Increasing the chances of the entry of other moral perspectives and
experiences into computer sciences is likely to generate benefits to
others beyond those who wish to work in computer sciences. This
could include both the subjects of computer sciences research and
wider groups of individuals, including disenfranchised and
marginalised people. Moral qualities prized by CE are often
important in regard to the rightful treatment of individuals in
research and experiments, especially those who are more
vulnerable, such as transgender individuals, children, and some
people with disabilities. HCI researchers may, to take just one
example, study the needs of older people, including those with
dementia, for attaining degrees of digital literacy and enrichment
[62]. Due in some part to implicit biases, older adults are an
example of a group that tends to get overlooked in relation to new
technologies.
Care ethicists themselves have often focused on various groups
who have been socially marginalised, subjected to prejudice, and,
furthermore, relatively neglected by mainstream normative theories.
Eva Kittay, for example, has used CE to stress the moral necessity
of caring in the right way not just for individuals who are capable
of achieving a full range of human flourishing, but also and vitally
those with severe cognitive impairments whose opportunities in life
are comparatively, and sometimes profoundly, limited [27]. Kittay
highlights the value of such caring relations to both the carer and
the cared-for. For thinkers like her, CE asks us to be just as attentive
to the needs of people struck by misfortune as to the interests of
people who have higher degrees of self-sufficiency, autonomy, and
independence [41].
Both CE and the moral experiences and perspectives of many
women lend themselves to a caring-style concern and sense of
justice for vulnerable individuals in the community who may be
more broadly affected by the computer sciences. Sometimes this
will involve a sensitivity to the effects of computer sciences on
women themselves. For example, close attention must be paid to
technologies which require the training of algorithms on data sets
that could introduce gender biases with potential negative
consequences for women in the general public. The same also goes
for technologies that could harm various marginalised and
vulnerable groups of people (and, as for example ecofeminists
recognize, nonhuman animals).
Identification and active correction of a range of ethico-social
problems is not only important because of the harm done to various
individuals. Ameliorating existing inequality also has the potential
to improve the quality of the science itself. Just as being attentive
to the song of female as well as male birds — a dimension of animal
behaviour overlooked because of the bias of male researchers —
enriches and improves ornithology, so too does heightened
attentiveness to biases, faulty assumptions, and prejudice
potentially enrich, broaden, and improve computer sciences. This
applies not only to the more human-centred subfields like HCI, but
also to more technical (and, as our results show, especially male-
dominated) subfields like CV and AI. Therefore, creating the
conditions for greater gender equality (and other kinds of equality)
across the subfields of computer sciences should be seen less as a
threat than as an opportunity for enhancing the rigour and value of
the discipline. On top of the ethical arguments, this last point about
the quality of the disciplines provides further support and additional
leverage for making changes to the current system to advance
gender equity.
5.3 Recommendations and Professional
Responsibilities
The CE framework supports not only the passive avoidance of, say,
discriminatory hiring practices, but also the active nurturing of
individuals and the modification of institutional attitudes and
structures that prevent women (and other minoritised groups) from
fairly occupying roles in the various subfields of computer science.
Furthermore, our discussion of how CE applies to computer science
can be used to justify and reinforce existing professional standards.
DRAFT COPY ONLY. PLEASE CITE FINAL VERSION.
The Association for Computing Machinery (ACM) Code of Ethics
and Professional Conduct [4], for example, contains several ethical
principles relevant to our discussion of gender (and other kinds of)
diversity. Thus, Principle 1.4 of the ACM Code says that
Computing professionals should foster fair participation
of all people, including those of underrepresented groups.
Prejudicial discrimination on the basis of age, color,
disability, ethnicity, family status, gender identity, labor
union membership, military status, nationality, race,
religion or belief, sex, sexual orientation, or any other
inappropriate factor is an explicit violation of the Code.
The use of information and technology may cause new,
or enhance existing, inequities. Technologies and
practices should be as inclusive and accessible as possible
and computing professionals should take action to avoid
creating systems or technologies that disenfranchise or
oppress people. Failure to design for inclusiveness and
accessibility may constitute unfair discrimination. [4]
Indeed, the ACM Code even says (Principle 1.1) that when “the
interests of multiple groups conflict, the needs of those less
advantaged should be given increased attention and priority.” [4]
The Code then, calls for resolute action to be taken on behalf of
individuals facing injustice and discrimination. Such a strong
stance is supported by a CE approach, with its particular emphasis
upon people in need of care and justice. Furthermore, the ACM
calls upon its members to (Principle 2.1) “strive to achieve high
quality in both the processes and products of professional work”
[4]. This provides another reason for striving to remove
disadvantage and injustice since, as we have argued,
underrepresentation of women (and other groups) can have a
negative effect on the quality and breadth of the work done in the
subfields of computer science.
Given the standards to which the ACM aspires under its Code of
Ethics and Professional Conduct, and given the arguments we have
presented from the ethics of care, we would argue that the
computing community has responsibilities and caring duties to
promote and support those standards. One concrete way to promote
this is for the ACM to monitor compliance with its principles and
to set up a dashboard of compliance against which computer
science communities can measure themselves annually, providing
accountability within their own community and to the computer
science community at large. This includes metrics of performance
and inclusion not only of gender, but also of race, disability, class,
sexuality and numerous other minoritised groups. Providing such
metrics will raise awareness and comparison between communities,
hopefully also leading to sharing of best practice and changes in
behaviour. Meanwhile, those communities already working on
diversity and inclusion and contemplating governance could share
their experience and strategies with other communities. In addition,
a best practice guide might be facilitated by the ACM. These are
just a sample of the concrete steps the ACM should consider.
Which other detailed responses should be pursued depends on
further research, including hearing the views of a diverse range of
stakeholders. Our key point in this preliminary study, however, is
that such concrete steps are urgent and necessary, and that they
should be guided by the sorts of attentiveness to and engagement
with minoritized individuals that CE so clearly brings out.
6 Future Work and Conclusion
This study is the first step in a bigger project to identify outputs and
cultures relating to fairness, accountability, and transparency
within the different computer science communities. To move this
project forward, it would be helpful to investigate what systems,
research, and active members address these issues, and to test the
hypothesis that more diverse communities will, in fact, have more
advanced systems, academic work, and conversations about
fairness, accountability and transparency. If this hypothesis is not
correct, it would be useful to explore what helps to advance these
conversations and considerations within individual communities.
Our hypothesis is based on research in other fields that have
indicated that diversity is key to good research.
Using this study as a steppingstone, we intend to undertake further
qualitative research to analyse what works in the various
communities, what does not, and how these successes and failures
can be better shared. The next step involves qualitative research
within each of the communities to ask minoritised members about
their lived experience and the cultures within each field, including
successes and failures, and what we can learn from both to achieve
the standards set out by the ACM code of ethics. Finally, we would
invite other scholars to investigate how we could fulfil our ethical
duties of care to the computer science and broader community.
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