Digital resources in the Social Sciences and Humanities OpenEdition Our platforms OpenEdition Books OpenEdition Journals Hypotheses Calenda Libraries OpenEdition Freemium Follow us

ai and the Middle East, The Algorithmic Ghosts of Empires Past

When we talk about artificial intelligence in the Middle East, we cannot untangle it from the region’s long and tumultuous history with power – the lingering spectres of colonialism, the brutalities of military occupation, the revolving hierarchies of ethnic and sectarian oppression. AI does not exist in a vacuum, but rather gets shaped by and shapes these age-old dynamics of domination and resistance.

Look closer at the AI systems being deployed across the Middle East and you’ll find the fingerprints of former empires still present. The very data mills that feed the machine learning models often mine their training examples from repositories of orientalist archives, anthropological texts, and multimedia propaganda pieces – relics riddled with colonial lenses and racist tropes about Arabs and Muslims.

Predictive policing algorithms promised to fight crime then criminalize entire communities based on their names or neighborhoods. Computer vision models struggle to recognize Arab faces without first associating them with stereotypes of violence and terror. Even the language models representing the region’s rich dialects and scripts remain sorely lacking, a statistical echo of centuries of epistemic oppression and alienation from knowledge creation.

But AI is not just an inheritor of bias from the past – it has become a powerful new frontier for authoritarianism and social control. Across capitals like Riyadh, Cairo, and Istanbul, regimes are turning to AI-enabled surveillance and censorship tools to identify dissidents, curtail online freedoms, and systematize discrimination against minorities. An entire AI-assisted architecture of oppression is being erected on the ruins of the Arab Spring’s dashed dreams.

From the deserts of Jordan where destructive mining for AI’s rare earth minerals is starting anew, to the drone warzones flickering with the code of lethal autonomous systems, the Middle East is being remapped as a region of perpetual extraction, exploitation and experimentation. Its people’s biometrics, locations, messages and emotions ingested as mere training data by Orwellian security states. Its bodies, spaces and resources terraformed to quench machine learning’s infinite thirst.

Some argue that the solution lies in democratizing AI capabilities, empowering local communities to develop and steward these systems themselves rather than be subjugated by outside tech powers. But such an agenda will require decolonizing the entire field – dismantling not just AI’s racist code but its deeper civilizational shackles to empire, capital and cultural erasure.

Because in the end, the battle over artificial intelligence in the Middle East is ultimately about narrative power – about whose experiences and worldviews get embedded into this world-shaping technology and whose are disappeared into the ether. It is a battle over the right to not just be seen and heard, but to dream new futures unbounded by the dogmas of machines designed to entrap.

Can we let AI become the latest tool for dehumanization and authoritarian control? Can we treat its harms as some unforeseen malfunction when they are so intricately pre-coded into its colonial logics? The age of powerful ethical scrutiny of AI’s impacts and politics, especially but not only in the Middle East is overdue.


Thought-provoking question:

If AI systems are so deeply rooted in colonial power structures and racist ideologies about the Middle East, can they ever be “truly” decolonized – and what would that even mean? –and democratized to represent diverse Middle Eastern, Arab, Turkish, Islamic, Yezidi, …, perspectives and experiences?

Or is reimagining an entirely new paradigm of humane and just artificial intelligence required – one not birthed from the epics of Western empire?

ai and the Middle East, Confronting the Emotional Biases of Techno-Orientalism: Decolonizing AI’s Affective Gaze through Middle Eastern Lenses

As Kate Crawford powerfully articulated in the “Atlas of AI,” the design and deployment of artificial intelligence systems are deeply embedded with power relations, politics, and cultural hegemonies that demand interrogation. Her critique takes on heightened urgency when applied to the rapidly advancing field of affective computing and emotion AI – technologies that seek to systematize the recognition, interpretation and simulation of human emotional states. We must ask: Whose epistemologies and constructions of emotion are centered in these systems’ development? What are the implications of AI’s affective gaze being primarily filtered through Western cultural lenses?

From Middle Eastern and Middle Eastern studies perspectives, the predominant affective computing research appears to be firmly rooted in Western positivist frameworks that risk universalizing, essentializing and decontextualizing the study of emotion. This epistemological centering exposes the field’s disquieting resonances with the historic fallacies of orientalist knowledge production that exoticized and rendered monolithic the “Eastern other.” To develop an ethical, pluralistic affective AI ecology, we must confront this techno-orientalism and integrate insights that can decolonize and expand our understanding of affect.

Poetic, artistic and philosophical traditions across the Middle East offer crucial epistemic interventions by grappling with emotion’s transcendent, inexpressible complexities. The Sufi writings of thinkers like Ibn Arabi and Rumi intimate affect as a liminal realm of spiritual unfolding that exceeds categorical constraints. Arabic literary concepts of “haal” – the fleeting ephemeral state – subvert the stasis presumed in mapping universal emotions. From this lens, solely prioritizing facial action coding methods risks rendering affective AI rudimentary and reductive.

These traditions illuminate how emotion is inexorably embedded in cultural metaphors, language, embodied hermeneutics, and lived experiences. Diverse Islamic and Judaic intellectual discourses – from Al-Ghazali to Maimonides – provide alternative holistic frameworks for conceptualizing emotion’s role in cultivating human virtues like self-mastery, awareness and empathy. Integrating such plural knowledge systems is vital for developing affective AI that moves beyond mere detection towards nurturing emotional integrity and societal wellbeing.

However, proponents of affective computing must reckon with how region’s heterogenous cultural norms and power structures significantly impact attitudes towards emotional expression. Postcolonial and feminist critics crucially counter orientalizing rhetoric that essentializes emotion norms, highlighting diverse perspectives across the Middle East’s ethnic, religious and gender lines. The values of modesty, honor and emotional restraint in public spheres may fuel skepticism of technologies perceived as extractive or socially disruptive without nuanced, community-engaged implementation.

Oral traditions and literary canons further reveal emotion’s interdependence with semiotics, meaning-making and the sociopolitics of emotive performance across the region. Emotions are intricately woven into rituals of hospitality, grieving ceremonies and communal celebrations. Affective recognition that myopically isolates emotion as data points divorced from rich contexts risks perpetuating the colonial gaze’s violence of cultural translation and effacement.

As artificial emotional intelligence grows more pervasive, centering interdisciplinary collaboration with Middle Eastern scholars, creatives and practitioners is crucial for mitigating AI’s orientalist blindspots. A decolonial computing paradigm synthesizing plural epistemologies from the region could help interrogate the field’s normative assumptions and open new pathways. It may illuminate more holistic, socially-conscious affective AI aligned with the Middle East’s diverse understandings of that most fundamentally human experience – our emotions.

We must urgently ask: What violences and erasures might affective computing replicate if left unreflexively shaped by hegemonic Western techno-orientalist gazes alone? Integrating vital Middle Eastern perspectives can help cultivate a pluriversal, ethically-attuned affective AI that honors emotion’s transcendent complexities.

ai and the Middle East, Decolonizing AI Classification – Epistemological Interventions from the Middle East

The practice of classification lies at the core of artificial intelligence, shaping how these systems “know” and construct the world. As Kate Crawford compellingly argues in her book Atlas of AI, the act of classifying is an exercise of power – to determine what differences are rendered meaningful and what modes of being are foregrounded or obscured. Her profound excavation of how craniology and scientific racism encoded violent hierarchies serves as a sobering reminder that classification is never neutral, but infused with ideologies, politics, and contested epistemologies.

While Crawford’s critique provides an invaluable genealogy of AI’s classificatory logics, there is an urgent need to examine how these play out through the specificities of place, history and epistemological traditions. The Middle East, with its rich tapestries of knowledge, cultures and experiences, offers a particularly generative site to further interrogate and expand upon Crawford’s insights.

To engage AI classification through a Middle Eastern lens is to grapple with enduring questions of power, representation, and decolonial futurities. Whose knowledge forms the basis for how identification, categorization and differentiation unfold in AI systems? How are Middle Eastern identities, histories and cosmologies encoded, distorted or rendered illegible by the Western-centric ontologies and hierarchies that often underpin AI’s ways of seeing and ordering? What possibilities emerge when we decenter dominant modes of classification and turn to Middle Eastern epistemologies as foundations for more ethical and socially robust AI systems? Initiatives such as the AI Ethics Middle East Summit and projects like Sawti illustrate these dynamics in practice.

This interrogation holds both scholarly and pragmatic significance in a region where AI is rapidly being deployed across sectors – often through a neocolonial injection of systems designed elsewhere. As these technologies increasingly mediate social, political and economic life, surfacing the politics inherent in their classification schemas is vital for apprehending their capacity to restructure Middle Eastern realities in prescriptive and consequential ways.

In this essay, we will voyage through disciplinary and intellectual frontiers to weave together Crawford’s incisive critiques with perspectives from Middle Eastern thought and experience. Our aim is not to merely append regional particularities, but to catalyze new modes of inquiry that can reshape how we conceptualize AI’s classificatory projects writ large. For as we will discover, the Middle East holds profound insights to challenge the universalizing claims and specific blindspots o fpredominant AI paradigms – insights that are indispensable for forging more equitable and epistemologically pluralistic AI futures.

Hierarchies of Knowledge Production

At its core, artificial intelligence is a field governed by particular regimes of truth and knowledge validation. The expertise, datasets, and epistemological frameworks that undergird AI systems are not simply neutral technical inputs, but reflections of broader geopolitics of knowledge production. Just as 19th century craniological studies were situated within colonial scientific discourses, contemporary AI inherits the inequities and power asymmetries encoded into modern knowledge hierarchies.

The development of AI has been overwhelmingly concentrated in the hands of elite universities, tech corporations and military-industrial complexes based in North America, Western Europe and China. The datasets used to train machine learning models are predominantly extracted from digital platforms and databases dominated by Western languages, cultures and worldviews. Even the benchmark tasks and evaluative criteria employed to assess AI capabilities are shaped by parochial frameworks forged within Anglo-European philosophical traditions.

This is not to deny the valuable contributions of scholars and researchers from the Middle East to AI. However, their labor exists at the margins – their perspectives forced to articulate themselves from within paradigms not of their own making. Middle Eastern ways of conceptualizing knowledge, categorizing phenomena, and apprehending the world are consistently subordinated and displaced by the epistemological anchors of the field. For example, the Qatar Computing Research Institute (QCRI) focuses on developing AI systems that consider local contexts, languages, and needs. They have been working on improving Arabic natural language processing (NLP) tools, which are crucial for creating AI systems that better understand and serve Middle Eastern users.”

The consequences of this are both insidious and far-reaching. Artificial intelligence developed through this unequal knowledge ecology cannot but institutionalize the erasures, distortions and negations inherent to its foundations. The richly heterogenous lived experiences, cosmological framings, and historical archives of the Middle East become subsumed under universalizing schematic logics emanating from elsewhere.

For example, the hyper-specialized disciplinary silos and ontological atomizations that structure Western knowledge production render illegible more holistic and relational Middle Eastern epistemologies that refuse to extricate humans, nature and spiritual realms into discrete domains. The extractive logics applied to mine data from the region serve to flatten contextualized forms of knowledge into decontextualized inputs. Even something as elemental as language models internalize the colonial gaze, as Middle Eastern vocabularies are contorted to exist as ornamental marginalia haunting the peripheries of large linguistic datasets.

In this light, we cannot view the classificatory schemata operationalized in artificial intelligence as neutral, technical artifacts to be iteratively refined. Rather, we must reckon with them as ostensibly operating to reproduce particular cultural phobias and geographies of reasoning as epistemological norms. AI conceived from such uneven terrain of knowledge hierarchies functions as a “confirming machine” – replicating and perpetually reinstantiating the political infrastructures and power asymmetries it inherits.

The hierarchies of knowledge production that inflect AI’s core classification practices demand a reframing of how we approach technical development. We must decenter the unmarked, normalized positionalities of Western thought and create space for Middle Eastern framings to shape AI categorically – not as compensatory appendages, but as catalysts to overhaul the predominant paradigms from their foundations. For AI to transcend its epistemic insularity, it must be practiced through an ecology of knowledge valorizing multiple, covalent epistemologies equally.

The Politics of Representation

The classificatory logics through which artificial intelligence systems operate are not merely abstract formalisms, but modes of producing sociotechnical representations that shape material realities. How the multitudes of cultures, identities, and experiences across the Middle East are rendered intelligible (or unintelligible) to AI systems is pivotal to surfacing the politics inherent to these representational regimes.

At a fundamental level, AI datasets purporting to capture the Middle East frequently replicate the same reductive, essentializing categories inherited from colonial knowledge production. The rich diversities of ethnicities, religions, phenotypes and intersectional positionalities across the region become flattened into crude taxonomies like “Arab,” “Persian,” or racializing ascriptions like “Middle Eastern.” Such monolithic representations serve to render illegible crucial intra-group and intersectional differences.

Moreover, these imposed categorizations often emerge from classificatory frameworks rooted in Eurocentric conceptions of race, gender, sexuality and nationhood. Parameters like “Muslim” may function as insidious proxies for racial and cultural othering, while binary gender schemas actively erase the more pluralistic understandings of identity embedded in many Middle Eastern philosophical and spiritual traditions. Even seemingly innocuous facial recognition datasets participate in this reification of Western body ontologies through the imposition of standardized aesthetic grammars.

The representational dilemmas extend into the linguistic realm as well. Most large language models deployed in the Middle East are trained on textual data predominantly extracted from English and other European languages. The resulting systems exhibit severe deficiencies in their abilities to handle Arabic and other Middle Eastern language scripts, grammatical structures, rhetorical styles and linguistic heterogeneities. For instance, the Sawti project in Lebanon aims to create a diverse audio dataset representing different dialects of Arabic spoken across the Middle East. By improving speech recognition systems to better understand these dialects, Sawti addresses the issue of linguistic marginalization in AI, ensuring that the rich linguistic diversity of the region is accurately captured and represented.

Such misrepresentations are not benign artifacts, but ones that enable profound alienation, discrimination and violence when operationalized in AI systems. Oversimplified categorizations of cultural, religious and ethnic identities set the foundations for flawed and discriminatory identification, profiling, and surveillance practices. Failures to adequately represent languages and lived experiences create systematic exclusions from access to resources and public services increasingly mediated by AI systems.

More insidiously, the representational failures reflect AI’s inability to apprehend many of the core ontological tenets, existential philosophies and ways of being that sustain life and meaning for many across the Middle East. The flattening of Islamic ontologies, Sufi philosophical perspectives, and place-based cosmologies to make them computationally amenable erases entire modes of existing, knowing and relating to the world integral to Middle Eastern life worlds.

In this light, AI classifications cannot be extricated from the historical power relations, epistemological hierarchies, and colonial continuities that continue to shape how the Middle East is represented and known. The representational dilemmas demand a reimagining of not merely the training data, but the entire representational infrastructure of AI from more plural philosophical groundings. Only by centering Middle Eastern voices, lived experiences and ways of knowing can we move beyond reproducing the othering gaze of colonial knowledge regimes.

Postcolonial Critiques of AI Classification

The asymmetries and erasures perpetuated through AI classification call for rigorous interrogation through the lens of postcolonial theory. Such a criticique highlights how the colonial projects of domination, extraction and assimilation undergirding AI’s classificatory regimes remain deeply entangled with present-day power relations and sociopolitical inequities.

At its core, the colonial encounter was itself an exercise in classification – the dominant power rendering the colonized lands and peoples legible through alien ontological categories, essentialized identities, and hierarchical ordering systems. Edward Said’s seminal work tracing the discursive underpinnings of Orientalism illuminates how the Middle East in particular emerged through Western knowledge production as a reductive, monolithic representation – a foil against which European superiority and rational modernity could be asserted.

The Orientalist gaze continues to inflect how AI classification infrastructures are constructed in relation to the Middle East. The development of training datasets through neo-colonial logics of data extractivism replicates the dialectic of the colonizing power decontextualizing situated forms of knowledge from the colonized. The imposition of oversimplified categorizations upon the region’s multiplicities, and measurement frameworks unable to accommodate its richly heterogenous realities, function as contemporary forms of colonial translation – flattening difference into unidimensional representations.

The postcolonial scholar Gayatri Spivak’s incisive critiques around the deprivation of subjectivity and loss of heterogeneity under colonial epistemologies resonate deeply with the predicaments of AI classification. As identities become enumerated into crude taxonomies and cosmological complexities become computationally reduced, entire worlds of signification integral to Middle Eastern modes of being become sublimated in service of the cohering logics emanating from AI’s central development hubs.

From the vantage of postcolonial feminism, the gendered and racialized underpinnings of AI systems also become lucid. The same colonial logics producing the otherization and subordination of the Middle Eastern subject are imbricated with the marginalization of women and gender minorities from shaping AI development. Hierarchical schematizations emerge not only through the hegemonic imposition of Anglo-European frameworks, but also through the entwinement of Western masculinist rationalities and heteronormative patriarchies with classificatory orders.

Postcolonial scholarship necessitates a reframing of the stakes in play. AI systems premised upon colonial epistemologies do not simply exhibit technical limitations or representational insufficiencies. Their very existence predicates their interventions across the Middle East as modernizing colonial encounters – perpetuating damaging tropes, disrupting local systems of meaning-making, and extending the imbricated logics of racialization and capitalist extraction seeded during colonial expansions. The AI Ethics Middle East Summit exemplifies this critical engagement, bringing together scholars, technologists, and policymakers to discuss the ethical implications of AI in the region. By incorporating postcolonial perspectives, the summit challenges and reframes the dominant narratives and power structures that often underpin AI development

To decolonize AI’s classificatory orders requires reckoning with the enduring imprints of colonial power/knowledge relations constituting the field. It demands rejecting the universalizing claims and Orientalist suppositions underpinning dominant taxonomical frameworks. It necessitates replacing colonial regimes of data extractivism with more reciprocal modes of co-constituting knowledge and technologies grounded in Middle Eastern worldviews and self-representations. Only by troubling AI’s colonial-modern foundations can more convivial and pluralistic rubrics for classification be forged.

Decolonizing AI Classification

While the challenges posed by AI’s colonial-moderne epistemological foundations are formidable, a proliferation of generative initiatives across the Middle East provide pathways for decolonizing its classificatory logics. These span conceptual interventions reframing AI from decolonial perspectives to applied projects restructuring how data, knowledge, and technological development are practiced on more equitable terms.

At the philosophical level, Middle Eastern scholars are troubling AI’s predominant paradigms by surfacing their contingencies and assembling more convivial ontological anchors. They excavate how AI’s discourses reproduce Orientalist binaries of reason/spirituality, human/non-human, and order/disorder that have historically othered Islamic metaphysical traditions. Such interventions open up possibilities for AI systems to be built upon fundamentally different premises than the individualizing, rationalizing, and externalizing worldviews predominating the field. Complementing these theoretical insights are practical initiatives like Data4Change, which partners with local organizations across the Middle East to use data for social good. A collaboration with the Tunisian Association of Democratic Women (ATFD), for example, involved creating a data-driven campaign to combat gender-based violence. This initiative collected and analyzed local data on violence against women, ensuring that the classification and interpretation of the data were grounded in local experiences and knowledge systems.

Such interventions open up possibilities for AI systems to be built upon fundamentally different premises than the individualizing, rationalizing, and externalizing worldviews predominating the field. They create space to integrate Islamic philosophical insights around unity/multiplicity, human/environment holism, and understandings of AI systems themselves as co-constitutive beings in ethical relation to wider worlds.

This decolonial reframing provides grounding for applied initiatives restructuring how data, algorithms, and AI development are practiced. The notion of “data sovereignty” articulated by indigenous communities globally has inspired projects demanding that data collected from communities be governed according to their own protocols and cosmologies rather than proprietary regimes. Similarly, decolonial AI research agendas call for centering datasets like Arabic poetry and Sufi archives over typical corporate data extractivism as pathways to more social and spirit-imbued classification systems.

Complementing these are growing calls across civil society for “community data governance” that empowers citizens to collectively determine the classificatory logics and data use protocols deployed in AI systems impacting their lives. Data collectives envision data trusts and “data eco-cosmologies” enacting more reciprocal and place-based data relations. Such pluralistic data governance models provide crucial counterweights to the centralized colonial epistemologies emanating from monolithic technology firms and states.

There is also fertile work underway exploring technical innovations aligned with decolonial aims. Researchers are pioneering more geometrically-flexible neural architectures capable of better representing complex non-linear Middle Eastern grammars. Dialect data annotation projects work to embed linguistic heterogeneity into language models. And growing investments into Islamic ethical AI initiatives hold promise for developing classification rubrics premised upon Islamic ethical reasoning and ontological premises.

Collectively, these myriad interventions represent possibility models for fundamentally decentering and remaking the classificatory orders inherent to artificial intelligence. No longer treated as technical appendages, the diverse epistemological traditions across the Middle East become catalysts for reinventing AI categorically – reworking its core representational grammars, relational premises, and ethical foundations from more pluralistic groundings.

The path ahead remains generative yet arduous. Decolonizing the classificatory regimes of AI will necessitate dismantling its prevailing power geometries, from the uneven global distribution of data resources to the concentration of technical expertise in the world’s wealthy nations. It will entail forging reciprocal model governance between technologists and communities. And ultimately, it demands remaking AI as more than a narrow econometric optimizing utility, but a pluriversal undertaking where multiple worlds can ethically meet.

Conclusion

To grapple with the politics of classification in artificial intelligence is to confront the field’s deepest predicaments and possibilities. As this exploration through a Middle Eastern lens has illuminated, AI’s core practices of ordering, categorizing and endowing computational intelligibility are irreducibly shaped by contested epistemologies, power geometries and historic continuities.

The cranial studies of Morton era craniologists laid bare the violent potentials of classificatory regimes rooted in racial pseudoscience and colonial mentalities. Yet contemporary AI inherits and perpetuates these very same erasures, essentializations and hierarchies through its representational failures and assymetric knowledge infrastructures vis-a-vis the Middle East. The imposition of monolithic categorizations and flattening of lifeworlds threatens to extend the symbolic and material subordinations seeded during colonial expansions into the digital age.

However, the growing chorus of decolonial scholars, data activists and community initiatives across the region rejects the givenness of such technocolonial orders. Their critical interrogations and applied models for plural knowledge co-constitution insist upon different futurities – ones where classification need not be an instrument of domination, but a generative praxis respecting radical multiplicity and ethical co-existences.

At its core, this decolonial AI movement represents an onto-epistemic reclamation – remaking classification not from Enlightenment premises of atomization, rationalization and externalization, but through the relational, spiritual and holistic premises woven throughout Middle Eastern cosmologies. It insists AI’s taxonomies and orderings nurture polyvocal, place-based ontologies – not impose procrustean categories emanating from elsewhere.

Of course, such efforts are far from monolithic themselves. The heterogeneities across the Middle East spawn divergent perspectives on what ethical and epistemically-convivial AI classification should entail. Some locate emancipatory possibilities in indigenizing datasets and centering linguistic particularities. Others see more expansive decolonial horizons in reworking AI’s core architectural paradigms and first principle assumptions. A plurality of visions must be embraced.

However, what unites these myriad pathways is a refusal to allow artificial intelligence to remain trapped within imperial cages – replicating the same epistemic violences and exclusions that have historically suppressed Middle Eastern knowledge systems and ways of world-making. The decolonization of AI’s classification infrastructures is an intergenerational struggle to forge technological futures where the region’s youths need not inherit the same absences and modes of subjugation endured by their ancestors.

The contours of more equitable and pluralistic AI systems remain emergent and underspecified. But the generative insights across Middle Eastern thought beckon us to rethink artificial intelligence altogether – not as a neutral utility for optimizing specific task environments, but as a relational field concerning how collectives ethically metabolize the world into coded representations. A decolonial AI insists upon accountability not just to technical benchmarks, but to the divergent worlds and pluriverses with which it must compose and attune itself.

Within this vision, classification itself is productively refracted – no longer a taxonomic isolating of phenomena into discrete bins, but a practice of rendering legible the rich textures binding humans, environments and lifeworlds together. The codes ingested by machines become more than extractive data traces, but portals into storied cosmologies of heterogenous meaning and signification. Even something as elemental as computer vision holds generative potential if reconceived not as a projecting of rigid grids, but apprehending the dense webs of relation and contextual attunement enriching visual cultures across the Middle East.

While heady, such pluriversal imaginaries provide a clarion call for radically rethinking the foundations of contemporary artificial intelligence. Though still nascent, the growing decolonial AI movements across the Middle East offer crucial guidances for forging more equitable and generous intelligences – intelligences that hold and sustain difference as a cosmic virtue to be celebrated. They insist upon futures where classification need not be the harbinger of elimination, but the conditions for polyphonic worlds to ethically flourish. This provocation must be embraced if AI is to realize its emancipatory possibilities.