# Decoding The Digital Fukú: Deciphering Colonial Legacies to Critically
Assess ChatGPT in Dominican Education

Anaelia Altagracia Ovalle
Department of Computer Science
Universiy of California, Los Angeles
<EMAIL_ADDRESS>
###### Abstract

Educational disparities within the Dominican Republic (DR) have long-standing
origins rooted in economic, political, and social
inequity.111https://www.education-inequalities.org/countries/dominican-rep
Addressing these challenges has necessarily called for capacity building with
respect to educational materials, high-quality instruction, and structural
resourcing. Generative AI tools like ChatGPT have begun to pique the interest
of Dominican educators due to their perceived potential to bridge these
educational gaps. However, a substantial body of AI fairness literature has
documented ways AI disproportionately reinforces power dynamics reflective of
jurisdictions driving AI development and deployment policies, collectively
termed the AI Global North. As such, indiscriminate adoption of this
technology for DR education, even in part, risks perpetuating forms of digital
coloniality. Therefore, this paper centers embracing AI-facilitated
educational reform by critically examining how AI-driven tools like ChatGPT in
DR education may replicate facets of digital colonialism. We provide a concise
overview of 20th-century Dominican education reforms following the 1916 US
occupation. Then, we employ identified neocolonial aspects historically
shaping Dominican education to interrogate the perceived advantages of ChatGPT
for contemporary Dominican education, as outlined by a Dominican scholar. This
work invites AI Global North & South developers, stakeholders, and Dominican
leaders alike to exercise a relational contextualization of data-centric
epistemologies like ChatGPT to reap its transformative benefits while
remaining vigilant of safeguarding Dominican digital sovereignty.

##  1 Introduction

In Dominican lore, fukú describes the curse brought to Hispañola on slave
ships by Spanish imperialists in their pursuit of the New World. It is a
persistent doom casted upon the island and its inhabitants since Colonization,
reverberating throughout generations as manifestations of political, economic,
social, and personal misery [19, 18, 34, 36]. We ground this paper in the
colonial imaginary to center ways interlocking forms of coloniality have
endured across dimensions of Dominican life. The educational system in the
Dominican Republic (DR) is no exception, historically grappling with
instability and inequality stemming from historical colonial influences that
persist to this day. Colonial forces exhibit a fluid nature, manifestating in
the digital contemporary as digital coloniality [43]. This is marked by the
prevalence of data-centric epistemologies and technologies which entrench
power asymmetries [32], leading to critical AI fairness researchers raising
concerns on the potential for epistemic violence in AI-driven technologies
like ChatGPT. These technologies often exhibit a bias toward predominantly
white and Anglo-Saxon perspectives, favoring AI global north contexts while
marginalizing underrepresented voices [35; 40; 7]. Contextualized to the
Dominican Republic’s educational system, we identify parallels between
neocolonialism and present-day techno-optimism. Responding to existing work by
Dominican scholars who share enthusiasm for incorporating technologies like
ChatGPT into Dominican Education, this paper aims to assess risks in
manifesting digital fukú through the inadvertent relinquishing of Dominican
intellectual sovereignty via the uncritical adoption of these technologies.
While ChatGPT may contribute to expanded educational access, we strongly urge
Dominican public institutions like the Ministry of Education, AI developers
from the Global South, and, most importantly, Dominican citizens from various
socioeconomic backgrounds, to actively participate in a critical assessment of
these technologies within the context of their colonial histories. This
approach will enable a deeper understanding of the sociotechnical implications
they carry, so as to empower stakeholders to make more informed decisions
surrounding their digital agency.

##  2 Divisions between The AI Global North and AI Global South

The terms AI Global North and AI Global South are often used to describe the
disparities in the development, deployment, and impact of artificial
intelligence technologies between regions of the world [41]. The predominant
dissemination of AI Global North-oriented technology and AI governance reflect
resource inequalities, infrastructural gaps, and varying concentrations of
power [52; 37; 51]. This concentration of authority reinforces dominant values
[35] onto vulnerable communities, thereby exacerbating inequality and harm
[51]. This results in the creation of LLMs whose sociolinguistic boundaries
reflect formations of privilege and power [44; 47]. As just one example, the
most commonly used large language models (LLM) are pretrained on data
primarily sourced from English, anglo-centric texts located in North America
and the UK [23]. As a result, less resourced languages are more prone to
jailbreaking an LLM [56] and non-native English speakers are more likely to be
flagged for plagiarism by an AI system [30].

This digital privileging is reminiscent of colonial legacies which wiped
indigenous knowledge through coercive cultural assimilation and exploitative
practice. Generative AI systems derived by the Global North are developed and
trained within AI Global North contexts, holding societal impacts tied to
propagating stereotypes, representational and allocation harm, and cultural
erasure [51]. Empirical risk minimization (ERM), a theoretical foundation
underpinning many machine learning (ML) algorithms, is a general method for
fitting some parametrized predictor (e.g. linear regression, logistic
regression, neural networks) on real-world data. ERM finds model parameters
that minimize a user-defined loss function by taking its expectation,
resulting in an averaging of the loss over a training dataset. Yet, it is
through this expectation that majority groups are privileged in representation
learning, as minimizing average error fits majority populations [14; 6]. This
subsequent tyranny of the majority [13] leads language representations to
favor the majority groups. English, American culture and lexicon [23] are the
prevailing data used for LLM pretraining. Consequently, these behaviors lead
to direct and indirect harms on non-dominant groups, inclusive of vulnerable
and marginalized communities. It is through this severe power asymmetry that
we recognize parallels to coloniality that endanger a country’s structural
fabrics within AI Global South, including a country’s educational system.
Notably, this may occur even in the absence of malicious intent, even when
individuals act in good faith.

In practice, existing inequities are algorithmically exacerbated in many
forms. AI detectors consistently misclassify non-native English writing
samples as AI-generated [30]. Biases appear against minoritized groups, where
LLMs have displayed, for instance anti-Muslim [3] and anti-LGBTQIA behavior
[42]. [55, 48] found that LLMs overfilter voices from historically
marginalized communities during LLM detoxicifcation. Meanwhile, users are
increasingly using LLMs to analyze information, even though these models have
shown persistent inclinations to harms and hallucination with respect to
factual information [24, 58]. In the absence of diverse social and
infrastructural investment defending against an undue sense of trust and
dependence on these systems [51], dissemination of biased and inaccurate
information have large scale ramifications. Therefore, ensuring the
trustworthiness of AI necessarily calls for their careful situating within AI
Global South contexts. While several works have examined aspects of
coloniality in AI, none have contextualized what this may mean for the
Dominican Republic. In the following section, we provide a concise historical
overview of education in the DR, which later helps contextualize some
implications of incorporating ChatGPT into the DR educational system.

##  3 Dominican Republic: Historical Grapplings with Education and Agency

The Dominican Republic has consistently grappled with domination by external
entities. 222Initially colonized by Spain, the country gained independence in
1821, briefly occupied by Haiti from 1822 to 1844, until going under Spanish
rule again until 1865.. At the turn of the 20th century, the DR faced US
occupation between 1916-1924. During this time, the DR’s education system went
through several overhauls. In this section, while limited by space in this
paper, we do our best to identify a handful of threads which cogently unveil
how 20th century neocolonialism shaped education in the Dominican Republic. We
acknowledge that this recounting is not exhaustive.

U.S. Paternalism & White Supremacy At the turn of the 20th century, U.S.
concerns of geopolitical instability 333US investments in Dominican
agriculture, the Panama Canal were heightened by the DR’s debt to European
creditors and the looming First World War. In addition to the US consolidating
Dominican loans in exchange for control over DR customs, President Woodrow
Wilson authorized the 1916 military occupation as a preemptive measure to
protect U.S. assets, subsequently cementing the paternalistic role of the U.S.
[46]. While framing their actions as altruistic, several historical documents
authored by US military governors outline clear initiatives aimed at
dissolving Dominican autonomy and identity. Working-class adult Dominicans
were described as “mulatto peasants” and children which lacked mental capacity
for self-sovereignty, reflecting clear forms of explicit racial minoritization
[54; 1; 2; 46]. As such, overhauling the DR educational system was seen as a
priority so that individuals could better participate as citizens [46].
Simultaneously, Dominican intellectual elites, termed letrados, varied in
their opinion of the Dominican campesinos, or rural masses. Some embraced
their Black lineage and nueva raza while others fed white supremacist
ideology, characterizing lower social classes – often non-white – as “semi-
savage”, apathetic, and indifferent to their duties as citizens [33; 53; 46].
These letrados, in charge of instilling US mandates post-occupation, were the
same citizens advocating for increased immigration of Europeans so as to
dilute the DR’s African heritage [10; 33; 17].

Coercive Tutelage Notably, the DR iterated on educational reform prior to US
occupation, with its “Código Orgánico y Reglamentario de Educación Común”
issued in 1914 [8]. However, US officials introduced new reforms and a new
educational code that centered American values and ensured alignment with US
interests. This was enacted through the appointing of American allies in key
government positions and implementing educational reforms. Reminiscent of
Rudyard Kipling’s White Man’s Burden [27], the US undertook efforts to
“civilize” the nation through its teaching of democracy, citizenship, and
capitalism [46]. Between 1917 and 1924, the US addressed high illiteracy rates
in a coercive manner that further cemented social inequities. Redirecting
funds from secondary schools to rural primary school construction, US
officials solidified a labor division in the Dominican Republic; education
prioritized preparing future agricultural laborers, thereby perpetuating
existing social mobility gaps. The US introduced enrollment mandates that
obligated educators in the Dominican to police attendance and penalize parents
and guardians of school children, so as to fulfill these mandates [16; 22; 38;
33; 57].

Unsustainable Growth Rural schools grew faster than their ability to be well-
resourced. Letters between Dominican educational administration evidence how
they relied on the free labor from community members for rudimentary school
construction. Even then, schools had large student-to-teacher ratios, with
teachers paid up to 75% less than others who taught in grade schools [20; 46].
In an attempt to improve resourcing, U.S. military government imposed property
taxes, though this had limited benefit and only strained rural communities. As
a result, the military government permanently closed many schools and centers
for educator training, leaving Dominicans with gutted educational structures.

##  4 Assessing the Colonial Threads of ChatGPT for Dominican Education

In an analysis of ChatGPT for Domininan education, Bueno (2023) reminds
readers that capacity building is long overdue in the Dominican Republic,
optimistically framing the tool as a new avenue to educational resourcing and
outlines benefits such as 1) access to educational material 2) personalized
tutoring and 3) training instructors. Acknowledging ChatGPT’s potential to
influence pedagogy and educational access in the Dominican Republic, assessing
its actual benefits for students and educators relies on a thorough evaluation
within its existing education system. In this section, we position each
claimed advantage within the crossroads of DR’s neocolonial grapplings and
digital coloniality. By demonstrating how AI tools can perpetuate these
historical harms, our paper emphasizes that validating their benefits relies
on empowering DR constituents to critically assess these tools.

Access to Educational Material Bueno (2023) highlights ChatGPT’s potential to
widen access to educational materials otherwise previously inaccessible. While
ChatGPT holds potential for providing educational materials on-the-fly,
critical works in natural language processing (NLP) reveal the tendency of
LLMs to hallucinate, potentially leading to the generation of incorrect or
fake content [24]. Secondly, these works show significant deficiencies in LLM
abilities to retrieve facts in non-English languages [49; 25]. Therefore, non-
English speakers engaging with the tool are more likely to consume low-quality
or misleading information. Furthermore, access to “educational material”
presupposes that text outputted by ChatGPT is universally relevant across
cultural contexts. However, knowledge is not neutral; every LLM operates
within specific frame of reference [51]. As such, the success of an LLM like
ChatGPT hinges on the quality and diversity of its pretraining data so that it
can operate across contexts. And yet, LLMs like ChatGPT predominantly learn
from texts from or sponsored by the AI global north (e.g. preference
annotations) [40; 23]. Fraught with familiar epistemic violence through the
erasure of indigenous voices, LLMs which train on these hegemonies output
“material” which can perpetuate power imbalances through the privileging of AI
Global North values, norms, discourse, and knowledge which need to be
scrutinized. Therefore, in assessing ChatGPT’s viability for DR education, the
“view from nowhere” must be rejected through the sociohistorical
contextualization of these technologies.

Personalized Digital Tutelage Bueno (2023) points to ChatGPT as a new resource
for assisting students in exam preparation, delivering real-time homework
feedback and enabling personalized learning. While the technology holds
promise, concerns linger in its impact on student learning and understanding
student progress. For instance, potential plagiarism resulting from the LLM’s
generated content necessitate new approaches to fairly assessing student
knowledge [31; 15]. However, failing to incorporate critical sociocultural
context in these approaches, especially if they incorporate AI tools, risks
harming students. Namely, LLMs used for plagiarism detection exhibit biases
against non-native English writers, resulting in their marginalization in both
evaluative and educational settings 30. This highlights yet another aspect of
harm caused by Anglo-centric LLMs within multilingual contexts [21; 25; 47].
English’s global dominance stems from a historical process of militarized
colonization, raising concerns about the erosion of multilingualism, language
and culture rights, and perpetuation of marginalization through generative AI
systems [51]. Meanwhile, scholarship which explores the benefits of ChatGPT-
based tutoring remain within a western context [31], which in turn, raise
further concern over its ability to recognize alternative worldviews and
historical accounts. Risks of imposing particular forms of historical
knowledge by AI tools are reminiscent of the DR’s grapple to maintain a united
sense of identity and agency [50]. Given aforementioned dependence on diverse
training data, one may reasonably question the extent to which these tools can
offer tutoring contextualized to varying sociohistorical positionalities,
inclusive of those from the margins. Parallels nostalgically emerge between US
military government in DR celebrating the expansion of schools as their own
achievement, while community narratives offer a differing viewpoint which
centers achieving this through local community efforts [45]. Within techno-
optimistic discourse, it is critical to approach new AI tools with reasonable
skepticism, especially if students may be privy to heavily rely on digital
tutoring. Therefore, the prospect of AI tool adoption in DR education requires
a careful examination of their contextualized tutoring benefits, the imparting
of critical AI assessment skills to students, and new methods to fairly assess
student knowledge [31].

Teacher Aids and Training Another claim by Bueno (2023) centers how ChatGPT
can assist teachers in administrative work and professional training. LLMs
like ChatGPT have the potential to revolutionize these aspects, though
scholarly discourse on computer science and education describe the success of
AI education as closely dependent on the readiness of teachers and their trust
in AI tools [5; 12]. Situated in a historical context, DR educational capacity
building has faced substantial demand. Meeting this demand necessitates a
comprehensive understanding of educators’ needs, capabilities, and
constraints, shaping case-specific guidance [26]. Open educational resources -
like tutorials, studies, and guidelines - offer a way for educators and
institutions to gain knowledge about using LLMs in education [26]. For
instance, learning to differentiate between model-generated and student-
generated answers is a valuable skill in AI-assisted pedagogical practice.
Nevertheless, exclusively providing this training is no substitute for
essential educator needs, such as fair teacher salaries and addressing
persistent structural inequalities in educational funding and maintenance.

Safety Bueno (2023) emphasizes the need to secure the storage and protection
of student personal information while using ChatGPT, raising concerns about
third-party access. However, ChatGPT already owns user-submitted queries,
thereby necessitating a reconceptualization of privacy and safety within this
context. Reassessing safety implications within the existing Dominican
educational system presupposes clear definitions of digital privacy, agency,
and safety infringements [39]. In this regard, gaps in digital literacy also
hold significant risk to both the digital sovereignty of the user and their
wider community. Previously mentioned scholarship detailing AI hallucinations
and possibilities for consuming and propagating misinformation raise
concerning questions around safety within and across these resolutions. If
relying on ChatGPT as a sole educational resource, rather than an auxiliary
resource, this concern for safety only amplifies [28]. Addressing these safety
concerns requires building capacity which critically drives transparent
discourse surrounding digital sovereignty to both educators and parents alike.
One approach may include dedicating resources for educational seminars and
audits by educators, educational staff, and students so that they learn about
data privacy, regulations, ethical concerns and best practices surrounding
data protection [26]. Furthermore, despite being presented as a transformative
tool free of charge, dimensions of invisible data labor by users paradoxically
reify the notion that user data - the driving force behind machine learning -
is exclusively created by corporations. Consequently, this reinforces the idea
that corporations should be the sole custodians of such data. For instance,
eliciting user preferences with a thumbs up or thumbs down after ChatGPT
generations is one data point consumed for RLHF training [4, 29, 11]. While
one may argue that free user-tailored systems already brings substantial
benefits to users, masking this data labor prevents broader public discourse
concerning the equitable distribution of gains within the data economy. In
unveiling these aspects, addressing safety concerns goes beyond merely
acknowledging the need to secure personal information. It necessitates both
questioning and understanding the safety implications behind (1) who is
responsible for safeguarding personal data (2) how is that data used, and (3)
our capacity to recognize potential misinformation and manage unsafe or
adverse AI-driven outcomes.

##  5 Conclusion

The Dominican Republic’s colonial history reveals important historical
considerations for assessing ChatGPT for Dominican education. While the
prospect of integrating the AI-driven tool for improved educational access
drives speculative optimism, validating its benefits on-the-ground relies on
DR constituents holistically assessing these tools for themselves. This
requires centering the exercising of digital agency in praxis; empowering
Dominican citizens to critically assess AI-driven systems requires digital
capacity building that is supported by structural initiatives aimed at
broadening access to AI technologies in a way reflects the will of its
constituents.

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