# Social Inclusion in Curated Contexts: Insights from Museum Practices

Han-Yin Huang<EMAIL_ADDRESS>Delft University of TechnologyThe
Netherlands and  Cynthia C. S. Liem<EMAIL_ADDRESS>Delft University of
TechnologyThe Netherlands

(2022)

###### Abstract.

Artificial intelligence literature suggests that minority and fragile
communities in society can be negatively impacted by machine learning
algorithms due to inherent biases in the design process, which lead to
socially exclusive decisions and policies. Faced with similar challenges in
dealing with an increasingly diversified audience, the museum sector has seen
changes in theory and practice, particularly in the areas of representation
and meaning-making. While rarity and grandeur used to be at the centre stage
of the early museum practices, folk life and museums’ relationships with the
diverse communities they serve become a widely integrated part of the
contemporary practices. These changes address issues of diversity and
accessibility in order to offer more socially inclusive services. Drawing on
these changes and reflecting back on the AI world, we argue that the museum
experience provides useful lessons for building AI with socially inclusive
approaches, especially in situations in which both a collection and access to
it will need to be curated or filtered, as frequently happens in search
engines, recommender systems and digital libraries. We highlight three
principles: (1) Instead of upholding the value of neutrality, practitioners
are aware of the influences of their own backgrounds and those of others on
their work. By not claiming to be neutral but practising cultural humility,
the chances of addressing potential biases can be increased. (2) There should
be room for situational interpretation beyond the stages of data collection
and machine learning. Before applying models and predictions, the contexts in
which relevant parties exist should be taken into account. (3) Community
participation serves the needs of communities and has the added benefit of
bringing practitioners and communities together.

Curation, museums, libraries, social inclusion, diversity

††journalyear: 2022††copyright: rightsretained††conference: 2022 ACM
Conference on Fairness, Accountability, and Transparency; June 21–24, 2022;
Seoul, Republic of Korea††booktitle: 2022 ACM Conference on Fairness,
Accountability, and Transparency (FAccT ’22), June 21–24, 2022, Seoul,
Republic of Korea††doi: 10.1145/3531146.3533095††isbn:
978-1-4503-9352-2/22/06††ccs: General and reference Design††ccs: Computing
methodologies Philosophical/theoretical foundations of artificial
intelligence††ccs: Applied computing Arts and humanities

##  1\. Introduction

In many applications of AI, machine learning technology is employed to refocus
the attention of humans. In many classification tasks, the intention is to
automatically label digital objects in ways that scale beyond human capacity.
The necessary standardisation in this procedure has both been lauded for its
efficiency, yet criticised for its taxonomic crudeness, which tends to be
particularly oblivious to cultural and contextual sensitivities and minority
perspectives.

Further downstream, machine learning technology empowers how vast collections
of automatically describable digital objects get organised and re-organised,
and how, when a human seeks to interact with the collections, they will be
filtered down to much smaller-sized ‘relevant’ sub-selections. In digital
libraries, search engines and recommender systems alike, this has again both
been lauded and criticised. On the positive side, automated filtering
technologies can traverse digital collections more broadly and efficiently
than humans can do. Generally, through digitalisation and unified access
mechanisms, collection items that would be hard to reach in physical archives
can now universally and globally be retrieved. Yet, on the negative side,
filtering and access mechanisms in our digital information space may not
actually capitalise on the diversity they could in principle offer. Many
services do not naturally stimulate their audiences to venture beyond the
filtered sub-selections offered to them, and perspectives provoking the
strongest engagement easily get prioritised, at the risk of favoring
‘majority-group’ or heavily polarising world views.

Often, we face large collections reflecting a pluriformity of perspectives and
interpretations, while people interested in interacting with these collections
will only get to see limited selections of items. How can such selections be
constituted and presented in ways that do right to the original diversity in
the broader collections? How can curation be performed in ways that respect,
engage and include audiences beyond mainstream perspectives, without
necessarily going down populist routes? In digital information access
applications, we are starting to recognise these are relevant, yet still
unsolved challenges. When not applied consciously, AI technology may however
actively work against these challenges, and it still is an open question how
to avoid this. However, in the physical world, over the last decades, similar
questions and challenges have actually been posed and addressed in the museum
community.

For at least two decades, the museum sector has been confronted with debates
on its social role, potential biases in its work and issues of social
inclusion and cultural diversity (Matarasso, 1997; Newman and McLean, 1998;
Dodd and Sandell, 2001). According to the current museum definition of the
International Council for Museums (ICOM), a museum is an institution that
‘acquires, conserves, researches, communicates and exhibits’ both the
intangible and tangible heritage (ICOM, 2007). In this process, a museum
decides what is accepted into its collection and what is deemed significant
heritage for the past, present and future generations. Museums are, therefore,
institutions with the power to endorse views.

Traditionally, museums are seen as places that house and exhibit items of
historic, scientific or aesthetic values, where the criteria primarily have
been based on the dominant groups whose cultures museums were founded on. Such
a curating process can actually be interpreted as one of exclusion (i.e.,
deciding what does _not_ get to be accepted into a collection, and what thus
would not be deemed significant heritage). Recognising this, various
activities and actions have been initiated to still foster social inclusion,
as will be discussed in this paper.

Beyond these various initiatives in different museums, a recent debate
surrounding the museum definition proposal of ICOM illustrates the sector’s
attempt to officially formalise inclusive practices as part of its raison
d’être. Before its general assembly in 2019, ICOM announced that it would put
a new museum definition proposal to be voted on during the assembly. This
proposed new definition referred to museums as ‘democratising, inclusive and
polyphonic spaces’, whose aims are to ‘contribute to human dignity and social
justice, global equality and planetary wellbeing’ (for Museums, 2021). While
applauded by supporters, the announcement was also met with criticisms, among
which the lack of transparency in the decision-making process and insufficient
consultation with members and national committees came to the fore. The 2019
ICOM general assembly closed with a postponement of the vote. A series of
consultation sessions, led by the ICOM Define committee and scheduled to take
place until the first quarter of 2022, ensues. According to the analysis and
report of the ICOM Define Committee’s second consultation with national
committees, among the terms mentioned by national committees, many are related
to social inclusion and diversity, for example, ‘inclusive’ is mentioned by 66
percent of the national committees, ‘open to society/public’ 52 percent,
‘community/society’ 51 percent, ‘accessibility’ 45 percent, ‘service to
society’ 44 percent and ‘diversity’ 41 percent (Committee, 2021). Among the
five draft proposals for museum definitions put forward by ICOM in February
2022, museums being inclusive and accessible to a variety of audiences is
mentioned in all five definition proposals111https://icom.museum/en/news/on-
the-way-to-a-new-museum-definition-we-are-doing-it-together/, retrieved April
30, 2022. National committees have been asked to identify their preferred
proposals in the fourth and final stage of consultation. The final report is
scheduled to be published in May 2022..

Both in the museum sector and in AI-powered digital information technology,
potential biases in curated contexts pose very tangible challenges. Both
worlds have a similar process, that starts with collecting what is deemed
relevant to their purposes: in the museum’s case, the tangible and intangible
heritage, and in case of AI technology, the data. The collections are then
interpreted, after which selected items and views are presented to audiences.
Drawing on this parallel, we argue that the existing debates and reform
movements with regard to the museum experience can provide useful insights for
developing socially inclusive approaches in AI.

In their work on Lessons from Archives, Jo and Gebru draw attention to how
sociocultural data is collected and processed in archives (Jo and Gebru,
2020). They identify principles that the machine learning community can learn
from, in particular on practices to increase transparency with regard to data
collection. In this paper, we complement these insights by taking a broader
process and organisational perspective, looking beyond the collection phase,
and also including the interpretation, application and presentation phases,
that ultimately reach human audiences.

##  2\. Social Inclusion in Museums

Although, because of its more positive note, _social inclusion_ is a term
applied more often in policy and in practice, it is also used interchangeably
with social exclusion, social cohesion, solidarity, integration and social
capital (Newman and McClean, 2004; Silver, 2015). First coined in France in
the 1970s, _social exclusion_ referred to the marginalised groups of the
French welfare system and has since then become part of the European political
discussions. As time went by, the discussion in policy moved to the more
positive ‘social inclusion’ (Silver, 2015). According to Silver, social
inclusion has different connotations and applications in different settings on
the national and neighbourhood levels, and is also located in place (Silver,
2015). Social inclusion is therefore a context-specific term. To many cultural
institutions, what social inclusion entails is extremely fluid, and it thus is
difficult to give a definition that fits all practices (Newman and McClean,
2004; Silver, 2015).

Richard Sandell is one of the early museologists who champions museums as
‘agents of social inclusion’ and suggests that museums nowadays are involved
in or committed to different degrees of socially inclusive practices and
ideals (Sandell, 1998). The inclusive museums engage from the cultural
dimension by tackling representation and access aspects in museum practice.
Museums as agents of social regeneration take on the social, economic and
political dimensions and seek to promote better quality of lives for
individuals, also encouraging personal development of the disadvantaged
groups. Finally, museums as vehicles for broad social change aim to encourage
positive social change and open themselves up as forums for public debates
(Sandell, 1998). Social inclusion in museum practice responds at first to the
discussions of museum accessibility for a wider range of visitors (cultural
dimension) and later includes the discussion the capacity of museums
empowering its visitors (social, economic and political dimensions).

Engaging social inclusion in the museum sector has become an important part of
the discussion about democratising the museum practice (Sandell, 2003). In
contrast to its traditional image being situated in a power structure that
posits museum staff as experts and a top-down practice model, the democratised
museum practice seeks to revisit accessibility to the museum not just in terms
of physical access, but also in terms of social, cultural and economic access,
changing its relationship with the communities that have long been
underrepresented by the museum sector. Democratised museum thinking is
manifest in the changes in the theorising of representation and meaning-making
in museum practices. As Mason outlines in the representational discourse of
social inclusion in museums practices, there are three strands of influences
that intertwine to make socially inclusive practice an important aspect in the
contemporary museum practice (Mason, 2004). As the targeted audience shifts,
from the elite educated and knowledgeable group to a more general public
without specialist knowledge, subject matters also shift to include folk life
and everyday collecting: the ordinary heritage that the visitors can recognise
themselves in. A second strand is the shift of Culture with capital C to
culture with small letter c, from the elitist culture to simply referring a
way of life. The third strand is the influence of the environmental movements
in the 1960s and discussions on these in the museum sector, in the form of new
museology (Vergo, 1989) for _ecomuseology_ (Davis, 2011). At the core of the
ecomuseum philosophy is the museum’s thinking beyond the confinement of its
four walls, building relationships with communities.

Development in museum learning theories also informs new perspectives in the
meaning-making of museum visits. Literature of how museum visitors make sense
of the messages conveyed in exhibitions and programmes suggests that there is
no one universal manner of museum visit experience. Visitors go to museums for
different purposes with various expectations (Falk and Dierking, 2013; Hooper-
Greenhill, 2000). It is therefore impossible for museums to create a ‘one
thing fits all’ programme. The visitors who come through the gate also bring
with them their own personal, cultural, social and political context. Every
visitor can experience a museum visit very differently.

To illustrate how these notions were implemented for socially inclusive museum
experiences, we discuss three key principles, which in our perspective can be
translated to parallels in AI practice. First of all, we describe how instead
of setting neutrality as the ultimate goal, an attitudinal change informed by
cultural humility may provide better guidance. Secondly, allowing room for
situational interpretation at different stages of the development process
ensures that different contexts are considered and acknowledged. This implies
that potential biases should keep being readdressed throughout the whole chain
of services; that is, not only at the data collection, but also during the
design, interpretation and implementation stages. Lastly, it is important to
actively engage communities so that the practice connects with the community
experience.

##  3\. The Museum Insights

###  3.1. Neutrality Revisited

As discussed earlier, to become more socially inclusive, a sectoral change is
needed to transform the traditional museum practice. This does not trivially
happen. Among the aspects identified by Sandell that inhibit the sectoral
change, he first points out the attitudes of museum workers that do not
subscribe to social inclusion (Sandell, 2003). To facilitate attitude changes,
Sandell suggests training programmes based on first-hand experience and guided
by the groups and communities, that have traditionally not seen themselves
represented in the museums. Such attitude change can be summarised as
‘cultural humility’. The concept of _cultural humility_ has originally been
promoted in health care and library and information science, to tackle
oppressive descriptions in the medical and archival practices, respectively.
It refers to the ability to acknowledge the influences personal backgrounds
could have on the practitioners and their practices, and finding appropriate
approaches to deal with these influences (Tai, 2020).

Museums do not exist in a vacuum and neither do the visitors. As Stijn
Schoonderwoerd, the general director of National Museum of World Cultures
(NMVW), points out, “Museums, although they are often seen as places solely
dedicated to beautiful things, and therefore as neutral and non-political
spaces, are players in the social and political arena too” (Schoonderwoerd,
2020). A museum collects, interprets and displays tangible and intangible
heritage in its care. Its decision-making process illustrates a perspective
curated and presented by the museum. The key word here is _a_ perspective,
rather than _the_ perspective. In this process, a museum makes decisions about
what the museum deems relevant to the institution in its exhibitions and
education and outreach programmes. Museums function, therefore, in curated
contexts that are not without implications of certain values.

Instead of emphasising how ‘neutral’ museums should be, an attitude of
‘cultural humility’ signifies changes in the curatorial practices of museums.
To facilitate an inclusive curatorial practice, the selection processes to
decide what goes in the collections are informed by a museum’s mission
statement and collection policy. The collection policy will be reviewed at a
regular interval to make sure the museum does not collect too many repeated,
similar items, and the policy keeps up with developments within the museum.
Changes can also be seen in the calls for decolonising museum narratives, that
go from seeing different cultures as the exotic others, to placing different
cultures in their own contexts. History and the heritage of the ruling and
dominant groups are not the only heritage considered ‘qualified’ for museum
collections.

One of the most telling examples in the museums’ efforts of putting cultural
humility to practice, is the re-examination of terminology used in the museum
sector. When an item is accepted into the collection, museum curators conduct
research into its material culture such as its origin, material, artist, maker
and previous collectors, and update relevant findings and literature related
to the item. Derogatory terms in the catalogue descriptions and exhibition
texts that are outdated in contemporary society are being identified and
updated through research projects in museum terminology. A good example of
this is the Terminology project by the Amsterdam
Rijksmuseum222https://www.rijksmuseum.nl/en/research/our-
research/overarching/terminology, retrieved April 30, 2022..

Figure 1. _Isabella_ , Simon Maris, c. 1906, oil on canvas. Rijksmuseum,
Amsterdam.

The Rijksmuseum’s Terminology project started in 2015 with the painting shown
in Figure 1. When the painting became part of the museum’s collection in 1922,
it was described as “East Indian type. Oriental girl sitting in an armchair”.
In the 1970s, the work was titled “Little Negress” by one of the curators
then. In 2015, this title set off a public debate; as a result, the Museum
assembled an inter-departmental team tasked to investigate the language used
by the museum. Although the task force originally gave the painting a new
title “Young Woman with a Fan”, further research into the documents and
communications in the Maris family archives unveiled in 2020 that the girl’s
first name is Isabella, and that she was 12 years old when she posed for
Maris. “Isabella” sparked a series of term changes in the museum, for
instance, ‘slaves’ are now described as ‘enslaved’ and ‘koelies’ are now
referred to as ‘contract workers’. Eurocentric terms have also been
identified; for example, ‘Indians’ are now referred to as ’original residents
of America’ and ‘exotic’, an adjective used to describe someone different from
the European norm, is now avoided by the museum.

Similarly, it is worthwhile to highlight the Words Matter project by the Dutch
National Museum of World
Cultures333https://www.materialculture.nl/en/publications/words-matter,
retrieved April 30, 2022.. This project produced a work-in-progress document,
compiling a list of terms that were previously used but are now identified as
derogatory, accompanied by various commenting essays (Modest and Lelijveld,
2021). It also discusses how terminology used in museums have impacts on its
work; not just in collection, but also in education and interpretation, and
its relationship to the community. Wayne Modest, one of the authors in the
Words Matter document, says in response to controversies in the Netherlands
about changing words and texts, that “Paying attention to words means
acknowledging that the words we use affect a person or a group feels excluded
or included, whether they feel a sense of belonging to society. This is about
representation, recognition and respect.” (Modest, 2020)

###  3.2. Situational Interpretation

As mentioned in Section 2, social inclusion is a fluid concept and its
definition and practice differ according to the context in which it is
applied. The museum practice of social inclusion goes beyond ensuring that a
collection has a diverse representation of its communities and audiences:
beyond the collection and curatorial departments, other areas of the museum
practice, such as the education and outreach departments, actively engage in
offering inclusive services. Different parts of the museum services provide
situational interpretation to respond to the needs of the audience groups they
are catering for.

Apart from changing the outdated languages used in the museum sector, as
outlined in Section 3.1, changes can be seen in how curators produce
interpretive texts of museum exhibitions with more visitor-oriented
approaches. Accessible texts that are easy for the public to read and relate
to, instead of jargon-loaded academic texts, are considered more effective in
getting the messages across. Moreover, there is no guarantee that the texts
and messages will be perceived and understood to the same degree and effect by
all visitors (Hooper-Greenhill, 2000). Understanding that meanings of museum
displays can be shifting from one visitor to another, museums start exploring
the inclusion of texts written by community members, and the engagement of
visitors in reflective questions in exhibitions. In New York, for example, an
exhibition by the New York Historical Society, Scenes of New York City,
displays art works alongside texts written by New Yorkers who are connected to
the subject matters of the works on
display444https://www.nyhistory.org/exhibitions/scenes-of-new-york-city-elie-
and-sarah-hirschfeld, retrieved April 30, 2022.. For an oil painting by
Gifford Beal which features a top-hat-wearing horse carriage driver, visitors
see both interpretive texts from the exhibition curator, and from a horse
carriage driver in Central Park. While the curator’s text introduces the
painter and touches on the carriage tour history at Central Park, the carriage
driver’s text is a personal reflection of spring time and being a carriage
driver in warm weather in the park, memories triggered by the painting
(Jacobs, 2021).

Similarly, when museum educators plan to utilise an item in collection as part
of their education programme or campaign, the research results (such a
descriptive texts in the catalogue or labels from past exhibitions) will form
the basis of this endeavour. They are, however, often adapted in order to
become suitable to the learning needs and specific contexts of the target
audience. In the process of developing these resources, the collaboration
between the curatorial department and the education department makes sure that
the produced texts or knowledge promoted is in line with the research result
of the curatorial department, but also incorporates the understandings of the
education department on how best to convey messages across to the target group
to achieve the desired learning outcomes.

With the museum collection as backbones, situational interpretation enables
different parts of the museum services to be bridges between the collection
and the visitors, by translating into languages or formats that visitors feel
comfortable with. An inclusive museum is not only constituted by an inclusive
collection, but includes a whole range of museum services that reach the
museum visitors.

###  3.3. Community Participation

In the effort to offer inclusive practices, the community plays an important
role, as informed by the new museology and ecomuseum philosophy. New museology
places museums in relationships with the communities in which they exist, and
serve at the centre of its discussion. The ecomuseum philosophy expands on
this and argues that museums should not be confined to its four walls. Instead
of a museum building, an ecomuseum operates within a territory, instead of a
building, and has a close tie with the communities whose lives are attached to
this territory (Corsane et al., 2007). Museums serve, therefore, as a ‘contact
zone’ where the different contexts, narratives and complexities converge
(Witcomb, 2003).

In order to have more diverse community narratives in the museums, audience
development becomes an important aspect of museums’ public engagement. The
lack of a universal definition of what constitutes a diverse and inclusive
museum is evident in the audience development practice. For example, the
concept of community curators both refers to a method of collecting, and a
strategy of audience development and outreach. This way of collecting is
usually a project-based, community-oriented approach to collecting. The
contexts of this collecting process are documented through recordings of
interviews of relevant parties and descriptions, so that these unique
decision-making processes are not lost. Such an approach is also a way to
build relationships with underrepresented groups.

Collecting Birmingham, for example, was a three-year (2015–2018) project by
the Birmingham Museums Trust in the city of Birmingham,
UK555https://dams.birminghammuseums.org.uk/asset-
bank/action/browseItems?categoryId=1978&categoryTypeId=2, retrieved April 30,
2022.. The first year of the project mainly consisted of consultation sessions
with focus groups to discuss different themes surrounding the participants’
life experiences in Birmingham. The Trust also recruited Collection
Ambassadors who promoted the project in different areas of the city. After the
first year, however, the Trust felt that most participants were already people
involved in heritage in some way, and looked to expand its participant
profiles. In the second year, the project team went to different venues and
events in the local neighbourhoods. More contentious objects related to race
or religion emerged as a result of the diversity of respondents in the second
year (Con, 2020).

Echt Rotterdams Erfgoed (Authentic Rotterdam Heritage, ERE hereon) is an
ongoing project started by Museum Rotterdam in the Netherlands and now is part
of the Stichting Wijkcollectie (District collection Foundation)
programmes666https://museumrotterdam.nl/ontdek/categorie/echt-rotterdams-
erfgoed, retrieved April 30, 2022.777https://wijkcollectie.nl/ere/, retrieved
April 30, 2022.. The ERE project started with a Volkswagen minivan (shown in
Figure 2) that belonged to a Bulgarian named Kamen. Kamen was travelling back
and forth between Bulgaria and The Netherlands with this minivan, transporting
his fellow Bulgarians who had been travelling between the two countries.
Museum Rotterdam considered this minivan to be an embodiment of the changing
Europe and city of Rotterdam. However, the question was how such an item could
be ‘collected’, while the owners still needed it for traveling across Europe.
Museum Rotterdam decided to number the minivan and register it in their
collection, but not to take it out of its running to place it in their depot.
After identifying an initial collection of 30 items, Museum Rotterdam called
on Rotterdam residents for additional proposals, and was overwhelmed by the
number of proposals received.

The Museum then decided to put out a call for Rotterdam residents to form a
collection committee. The committee has, since 2017, determined collection
criteria and met regularly for collection meetings. To date, one hundred
exemplars of Rotterdam’s living heritage have been included in this
collection, such as the Mayor Aboutaleb, who has been Mayor of Rotterdam since
2009, the Pauluskerk, a church that offers overnight shelters and warm food
for those in need, and the Summer Carnival Rotterdam, reflecting the
multicultural character of the harbour city.

In both examples, the museums rely on the local communities’ insights into
lives in their cities, and explicitly let the communities identify what
heritage matters to them. This raises attention to aspects that probably would
not have been covered from the museum practitioners’ perspectives alone.

Figure 2. _Kamen’s minivan_ , Object No. 0001 of Echt Rotterdams Erfgoed
collection. Photo Credit: Joop Reijngoud.

##  4\. Reflecting on AI practice

Having described multiple key principles and actions observed in the museum
community to promote social inclusion, we now reflect on ways in which these
also can be useful for AI applications. Here, we especially consider AI
applications that involve supervised machine learning procedures on data that
relies on human interpretation, and where intended applications will involve
curation steps.

###  4.1. Neutrality Revisited

As surveyed by Birhane et al., in many published research works in machine
learning, generalisation and efficiency can be recognised as core important
values defining achievement (Birhane et al., 2022). Denton et al. (Denton et
al., 2021) point out the common (often implicit) stance that more data
examples will lead to better learning outcomes. At the same time, dataset
construction historically has been less valued and incentivised in the
community, as also emphasised by Sambasivan et al. (Sambasivan et al., 2021).
Thus, available large-scale datasets have commonly been reused, becoming
‘naturalised’ and appearing value-neutral in the process (Denton et al.,
2021).

The stance that datasets—and the technologies built on them—are not actually
value-neutral, has only recently been gaining traction and debate in the
machine learning community (Scheuerman et al., 2021). However, this notion has
already been recognised as early as in 1996, in Forsythe’s paper outlining
insights from three years of anthropological observations in an AI development
project for migraine sufferers (Forsythe, 1996). The work illustrates the
hidden presumptions in developing an AI tool, arguing that different decisions
are made with different intentionality. Designers might think they cannot
build their own cultural backgrounds and values into the system they are
developing, but these explicit or tacit intentions often carry hidden
presumptions. These tools are not only ‘located’ in specific design settings,
but also in specific practice settings. The people who deal with data will
consciously or unconsciously be influenced by their own background and those
of others. By claiming to be neutral or striving to be neutral, the chances of
addressing the influences and biases would actually be lost. As argued by
Scheuerman et al. (Scheuerman et al., 2021), such an awareness in
documentation practices is key to changing the culture of dataset development
among the practitioners.

Thus, bias is not necessarily problematic per se, and always will be present
to some degree. Both professional archives (Jo and Gebru, 2020) and museums
will explicitly not seek to collect unbiased samples of the broad universe,
but will set an explicit collection policy, purposefully making choices
according to this policy and the institute’s overall mission statement. As
already argued by Jo and Gebru (Jo and Gebru, 2020), more transparency on the
policies and criteria behind machine learning datasets can be achieved through
more explicit policy documentation, and existing proposals on datasheets for
datasets (Gebru et al., 2021) and model cards for model reporting (Mitchell et
al., 2019) can naturally complement this. It will be beneficial to explicitly
write these with a mindset of cultural humility, accepting up front that the
currently presented world view is not the one and only, canonical world view,
and perspectives may need adjustment over time.

It should be noted that many ‘canonical’, heavily-employed, large-scale
machine learning datasets were explicitly not constituted with a mindset of
cultural humility. Multiple datasets in computer vision have been heavily
criticised for including problematic and offensive imagery (Birhane and
Prabhu, 2021) and labels (Crawford and Paglen, 2019), and displaying an
Amerocentric and Eurocentric representation bias (Shankar et al., 2017).

The follow-up to these criticisms had various forms. Where the work of Shankar
et al. on geodiversity issues both sparked more inclusive benchmarking efforts
(Sculley et al., 2019) and well-publicised commentaries drawing attention to
culturally specific AI biases (Zou and Schiebinger, 2018), Crawford and Paglen
pointing out offensive class labels in ImageNet (Crawford and Paglen, 2019)
led to those categories being removed from the ImageNet public dataset. Even
more radically, Birhane and Prabhu’s audit (Birhane and Prabhu, 2021) of the
Tiny Images dataset (Torralba et al., 2008) led to this dataset to be fully
and permanently taken offline.

As discussed in Section 3.1, it is not uncommon in museums that language and
representation of collections items used to be accepted at collection time,
but today would be considered derogatory and offensive. In reaction, this
language has been revised and updated; however, this does not necessarily have
to mean the previous language is fully erased. For example, the Words Matter
document explicitly addresses these terms, but with clear contextualisation of
how these terms today are problematic, and a clear underlying message that
these terms should not be used anymore. Contrasting this to the removal
actions undertaken on problematic machine learning categories and datasets,
while full permanent removal prevents future harmful automatic inferences, it
also fully cuts off the connection and context to earlier, existing models
that were trained on these datasets.

###  4.2. Situational Interpretation

The previous discussion raised that over time, descriptive language may need
revision, updating, and re-tailoring to the presently relevant audiences. In
the discussion in Section 3.2, we raised that for museums, these audiences can
be highly diverse, and in need of different languages and perspective
highlights. These notions lead to multiple reflections on AI practice.

First of all, in museums, the same collection item may be described in
different ways, or purposefully draw attention to different aspects of the
item. When seeing a picture of a carriage driver with a top hat in Central
Park, some people will focus on characteristics of the carriage, some will
reminiscence about being a carriage driver, and some may remember a similar
carriage trip elsewhere; these memories may be visual and factual, or abstract
and affective. They are very differently-natured responses, that jointly
constitute the experience to the item, while the physical representation of
the item itself remains the same. In an AI context, the item would have the
exact same encoded input representation, but a highly diverse vocabulary of
associated output labels. Would these be considered in a single, general
learning or tagging task, the same problems as encountered with ImageNet would
occur (Crawford and Paglen, 2019), where not all terms would have a visual
observable link, and some associations may not be desirable to learn.

Furthermore, considering the Isabella painting depicted in Figure 1 and
discussed in Section 3.1, without context, one may describe the painting as a
depiction of a seated young girl with a fan in fine clothing. In contrast, a
professional may describe it as an oil on canvas painting. However, knowing
the context of the discussion this painting sparked, it may now be described
as the art object that triggered the Rijksmuseum’s terminology revisions, and
used to be problematically titled. Again, this further strengthens the
perspective that single, universal, ‘neutral’ descriptions and representations
of the item should not be strived for. Parallel vocabularies, labels and
descriptions may validly exist for a single item, referring to different
sources and broader background contexts. Encoding these as multiple tags of a
single vocabulary, which often would happen in supervised machine learning
contexts, would both be too simple, and taxonomically problematic.

In terms of encoding parallel, polyvocal interpretations, richer labels and
annotations can be achieved by employing Web technology. The Semantic Web
naturally gives opportunities for richer and parallel linking of differing
viewpoints, as e.g. outlined by Van Erp and De Boer (van Erp and de Boer,
2021). Next to this, for digital music content, Solid Pods were proposed by
Weigl et al. as a means to link various content types (e.g. textual
annotations, scores, and recordings) at various levels of authority and
intended visibility (e.g. professional commentary vs. private notes),
facilitating provenance information, while explicitly keeping governance in
the hands of the owner (Weigl et al., 2020).

Beyond technological questions of encoding and representation, more
conceptually, questions can be asked about the nature and scope of
personalisation. In search engines and recommender systems, personalisation
typically happens in the back-end of a system as part of the filtering
process, leading to personalised curated samplings of a collection. Different
user profiles may be offered different selections of items to consume, that
may also be differently ranked.

An interesting notion in the museum practice is that the filtering process was
performed by a human curator, and the filtered sample of a collection for a
given exhibition will be fixed: everyone going to the same exhibition will in
principle be offered the same collection items to see. Any possible
personalised differentiation will be in the presentation and interpretation;
the parallel in the digital world would be in the front-end user experience.

With regard to this, the discussion in Section 3.2 very strongly emphasised
purposeful communication practice, that would link elements of curation and
education. In digital information access, tailored communication strategies
focusing on item presentation are still relatively rare. Furthermore, in
machine learning pipelines, deliberate connections across different stages in
the process—from collection through curation to presentation—are hardly seen.
Typically, datasets would be established in a general-purpose mindset: model
learning may be tailored to a given learning task, but the user-facing
presentation of the outcomes of this task are considered to be outside of the
scope of the machine learning procedure. The equivalent to the audience-
tailored museum exhibit experience, where end-user-specific front-end
requirements would affect criteria on data annotation and curation, has to the
best of our knowledge not been implemented as a process yet.

Especially in current works emphasising the importance of quality assurance on
data, calls for a more holistic take are emerging and increasing, explicitly
connecting practice downstream to dataset creation processes upstream. This
e.g. is the case for the proposal of Hutchinson et al. to more explicitly
include software development practices in dataset development (Hutchinson et
al., 2021), but also for Sambasivan et al.’s work on data cascades, showing
that uninformed choices made during dataset development will cause major
problems in downstream applications (Sambasivan et al., 2021). In addition, it
increasingly is acknowledged and recognised that more holistic connections
will need to be made between machine learning prediction in AI and user
experience (UX) considerations, e.g. in the call to action put forward by
Cramer and Kim (Cramer and Kim, 2019).

###  4.3. Community participation

In striving for social inclusion, museums have emphasised a pro-active focus
on communities whose voice has not yet been heard and uplifted. Translating
this to AI practice, care should be taken in doing this. Often, minoritised
communities were already implicitly involved in system development, but as
low-paid laborers, conducting data labeling tasks during the dataset creation
phase (Denton et al., 2021). This labeling procedure would normally not
solicit their personal perspectives, but rather ask for standardised
descriptions. Indeed, when participatory machine learning was coined as a way
to more inclusively involve communities, warnings were put out by Sloane et
al. that this should not lead to further exploitation and participation-
washing, where community work does not benefit the community, but rather
serves a party with power over this community (Sloane et al., 2020).

Looking at how the museum world implemented community participation, all of
the examples discussed in Section 3.3 clearly indicate agency of the community
itself. In the ecomuseum setting, the items of relevance to the community
explicitly remain in the context and under the governance of the community.
When inviting community members to perform community creation, it also is
clear the suggestions and insights will be respected, even if they may differ
from what an ‘authoritative’ curator would do.

Looking at participatory methods in the digital world, participation through a
citizen science perspective may be a better form than through a crowdsourcing
perspective. Where in the latter case, participant knowledge is solicited by a
requesting party, to benefit that party’s intentions, in the earlier case, the
participant is an actual, fully acknowledged participant, who will more
comprehensively join the full process leading to relevant insights. As e.g.
documented in Hsu et al. (Hsu et al., 2022), the community citizen science
approach can effectively include communities, engaging on concerns that
demonstrably are relevant to them.

Finally, the community participation practices in the museum world again
illustrate the importance of communication and education. Earlier, in Section
3.1, it was emphasised that cultural humility may not naturally be ingrained
in an institute, and minoritised communities will need to assist in educating
the institutes about their blind spots. The other way around, coming back to
Sambasivan et al. (Sambasivan et al., 2021), data cascades can be avoided when
data literacy is improved, which presently still gets little attention and
investment.

##  5\. Actionable organisational change: libraries in digital transition

Having built forth on the Lessons from Archives paper (Jo and Gebru, 2020),
and now discussing insights from the museum sector, we have been broadening
perspectives on social inclusion and AI from a wider range of the so-called
GLAM sector (galleries, libraries, archives and museums). While differently-
natured in their provided services, all these four institution types are
responsible for the collection, preservation, presentation and dissemination
of cultural heritage.

To further illustrate the relevance of GLAM sector experiences for current
social inclusion challenges in AI, as a final illustration, we wish to draw
attention to the library sector. In terms of its practice and servicing, this
sector may sit between the museum community and AI communities. Currently, it
actively goes through a digital transition in which former core public values
(e.g. ‘neutrality’) need revision and debate. In showing how this currently is
tackled in the public library community in The Netherlands, we again have an
inspirational example of how social inclusion in collection and curation
contexts is taken up as an organisational challenge, driven by explicitly set
policies.

The public library has historically been a source for knowledge and
information, purported to be offered in neutral ways. However, local public
libraries never would have the shelf space to store all the books in the
world. Therefore, collection policies have always actively been applied. With
digitalisation boosting online information access, today, the public library
may not be sought out as much anymore for knowledge. However, it still very
explicitly is a social meeting space, where people from different backgrounds
and perspectives may naturally encounter one another, where this would not be
trivial anymore in other aspects of daily life. Against the backdrop of these
developments, a dedicated Dutch law on public library facilities was passed in
2015 (wso, 2015), quoting independence, reliability, accessibility,
pluriformity and authenticity as core public values to uphold, and listing
five core library tasks:

  1. (1)

to facilitate knowledge and information;

  2. (2)

to offer possibilities for development and education;

  3. (3)

to promote reading and introduce literature;

  4. (4)

to organise encounters and debates;

  5. (5)

to acquaint the audience with arts and culture.

Looking at this, it is striking that ‘neutrality’ indeed is not mentioned, but
‘pluriformity’ is. Even more strongly, strategic library innovation agendas
especially emphasise the pluriformity aspect (Hermans and Oomes, 2019),
noticing that the public library is one of the few remaining trusted places
where confrontations with a different perspective than one’s own may be
tolerated.

Interestingly, the present eight ethical principles for using AI (Van Wessel,
2020), authored by the National Library of the Netherlands, still call for
neutrality as a core principle, explaining this as “not steering users into
directions that they may not choose outside of AI context”. At the same time,
the libraries voice explicit interest in digital recommender systems, and a
major concern voiced in strategic documents is that the application of AI in
filtering processes would narrow down a user’s perspective in undesirable ways
(Hermans and Oomes, 2019). In other words, the neutrality core principle needs
revision, and responsible AI recommenders in the public library context maybe
even _should_ actively steer users beyond the perspectives they would choose
without the recommender.

Beyond the possible role of AI technology in this challenge, similarly to
museums, libraries do not only seek to stimulate more inclusive practice as
part of a filtering or book-lending procedure. Considering the five core tasks
of a public library, organised social activities and cultural programming
explicitly are included as part of these tasks, and actively employed to
further engage relevant audiences. Here, the parallels with the community-
oriented activities of museums can easily be seen.

In line with their task to offer possibilities for development and education,
Dutch public libraries have today been designated as the place where citizens
can learn important skills necessary for daily life. This includes courses on
reading comprehension, but also increasingly focuses on digitalisation. Beyond
introductory computer courses, this e.g. includes assistance with making use
of digital public services, such as making vaccine appointments in the
COVID-19 pandemic. Furthermore, the public libraries also will be conducting
outreach projects to raise citizen awareness of AI. Increasing this awareness
is vital for allowing more inclusive and informed public debates on AI
technologies, their impact on society, and the true needs and wishes of users.
In the United States of America, for similar reasons, the ‘We are AI’ course
was developed and implemented as a collaboration between the Center for
Responsible AI at New York University’s Tandon School of Engineering, the Peer
2 Peer University, and the Queens Public Library, in the form of a learning
circle at the public library888https://dataresponsibly.github.io/we-are-ai/,
retrieved April 25, 2022..

##  6\. Conclusion

In this article, we reviewed how social inclusion practices have been
integrated in the practice of museums, particularly focusing on the perception
of ‘neutrality’, the need for situational interpretation, and the role of
community participation. We highlighted how mindset changes in the museum
world may be inspirational in tackling social inclusion challenges in AI
technology.

We wish to emphasise that the museum sector did not yet ‘solve’ matters of
inequality in representation and ownership. With the ICOM museum definition
discussion still ongoing, social inclusion has not formally been
institutionalised yet. Furthermore, some societal public museum statements
have been criticised to be lip service. For example, during the global Black
Lives Matter movement in 2020—in the middle of the COVID-19 pandemic and anger
surrounding the death of George Floyd at the hand of the Minneapolis
police—major museums such as the British Museum issued statements to express
solidarity with the movement. These gestures were, however, met with
criticisms that museums need to do better to turn words into deeds by re-
examining how Black communities have been portrayed. In the case of the
British Museum, for example, the Museum came under fire again for
controversial colonial-period objects within its collection, such as the Benin
Bronzes (Brown, 2021; Greenberger, 2021).

Criticisms on museums following the Black Lives Matter movement show that the
minority and underrepresented communities are not yet sufficiently satisfied,
and that the museum sector is still a long way from resolving issues of social
inclusion and cultural diversity in its practices. Still, at the same time,
many relevant discussions are being held, many policies have noticeably been
adjusted, and many tangible actions can be noticed. In our discussion of
these, it should be noted that conscious attitude changes are addressed at
different stages of the curation and presentation process, with a strong focus
on tailored presentation and its surrounding communicative intent.
Furthermore, the examples from the museum world also explicitly indicate how
social inclusion requires strong organisational commitment, and how different
departments within an organisation should actively be engaged in striving to
improve it, while sustaining a mindset of cultural humility.

In the AI community, the rising awareness of socioculturally specific
perspectives, interpretation dynamics, bias and power has so far largely been
concentrated at the side of dataset creation and documentation (Gebru et al.,
2021; Sambasivan et al., 2021; Denton et al., 2021; Hutchinson et al., 2021;
Miceli et al., 2022). However, in our current practice, commonly used datasets
may get repurposed for different downstream tasks and applications, where the
parties working on these tasks and applications may not always have been the
dataset builders. Furthermore, considering a final application, user-facing
experience considerations will likely be handled by a very different team than
the team working on data-driven components of the application. Thus, more
holistic quality assurance mechanisms will be needed.

Even more strongly, any communicative efforts focusing on application adoption
and acceptance may explicitly happen beyond technical application development
cycles. In our discussions of museum practice, we have highlighted that there
are good reasons to actively make these different departments connect, also in
the light of alignment with strategic mission statements. We illustrated how
the public libraries community in The Netherlands is presently going through a
similar transformation, involving a more profound organisational mission
reorientation, that manifests in a broad spectrum of associated activities.

Having positioned our current discussion next to Jo and Gebru’s Lessons from
Archives (Jo and Gebru, 2020), we have further strengthened the argument that
the AI community can learn from organisational best practices of GLAM
institutes. These parties build upon long histories of experience with
questions of curation, while at the same time being recognised as trusted, and
in many cases public, spaces. Many of these organisations currently are going
through digital transformations. While this is happening, they can provide
excellent environments for piloting more holistic takes on socially inclusive
AI technologies in practice.

###### Acknowledgements.

This work is part of the ‘Recommender requirements for pluriform perspectives
in local public libraries’ project (NWA.1228.192.235) of the National Science
Agenda (NWA) research programme, which is financed by the Dutch Research
Council (NWO). The National Library of The Netherlands (Koninklijke
Bibliotheek, KB) is a partner in this project. This partner role is that of a
relevant domain stakeholder, and is neither financially compensated by the
project grant, nor financially contributing to the project grant. In addition,
Dr Liem’s research time is supported through a personal Veni grant
(016.Veni.192.131) of the NWO Talent Programme. The NWO and the KB had no role
in the design and conduct of the research presented in this paper; access and
collection of data; analysis and interpretation of data; preparation, review,
or approval of the manuscript; or the decision to submit the manuscript for
publication. The authors declare no other financial interests.

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