In the ‘Data’ chapter, Crawford highlights how datasets used to train AI systems often decontextualize and dehumanize the people and cultures represented, treating them as mere ‘raw data’ to be extracted. From a Middle Eastern cultural studies perspective, how might we analyze this ‘extractive logic of data’ playing out in the region? What concepts or theories could help us re-examine data practices through a critical Middle Eastern lens?
Key Discussion Points:
- Unpack the colonial legacies and power dynamics embedded in taxonomies and classification systems imposed on Middle Eastern knowledge production
- Explore how oral/storytelling traditions and Islamic depictional norms may clash with textual/image biases of mainstream datasets
- Discuss relevance of theories like decoloniality, data sovereignty, situated knowledges to data ecosystems in the Middle East
- Analyze how diverse linguistic contexts (dialects, diglossias, scripts) create challenges for language data annotation
- Examine cultural attitudes around privacy, surveillance and ethical data collection across different Middle Eastern contexts
- Unpack potential gender, sectarian and other identity-based gaps, biases or sensitivities encoded in regional datasets
- Discuss role of indigenous knowledge traditions as underutilized data models to represent Middle Eastern contexts
Key Concepts:
Extractivism, Decontextualization, Data Genealogies, Decolonial AI, Anthropological Extractivism, Islamic Data Ethics
Potential Discussion Questions:
- How might a decolonial approach to linguistics and NLP data shift power dynamics around Arabic’s primacy versus minoritized languages?
- What ethical guidelines from Islamic jurisprudence or indigenous knowledge could inform responsible data collection in the Middle East?
- How can theories of “situated knowledges” help analyze cultural biases and omissions in mainstream datasets representing the region?
- What counternarratives could regional multimedia data sources like Arab cinema or hip-hop offer to combat stereotypical AI training data?
- How could consulting data genealogies excavate colonial influences on categorical systems used to label Middle Eastern data today?
Answers:
How might a decolonial approach to linguistics and NLP data shift power dynamics around Arabic’s primacy versus minoritized languages?
A decolonial linguistics approach would critically examine how the hegemony of Modern Standard Arabic in most NLP datasets and language technologies encodes linguistic imperialism and marginalization of minoritized Afro-Asiatic languages across the Middle East. It could draw from theories of linguistic relativity and situated knowledges to argue that dataset biases towards MSA efface the epistemologies and worldviews embedded in smaller community languages like Assyrian, Berber, Kurdish, and numerous Arabic dialects.
Decolonial projects could involve countering data extractivism by prioritizing participatory models of data collection that cede control to language communities themselves. This could help correct for centuries of colonial linguistic policies that suppressed diversity in favor of standardizing Arabic. NLP models trained on pluricentric data encompassing all language varieties could potentially unlock more equitable technologies attuned to the region’s linguistic heterogeneity.
What ethical guidelines from Islamic jurisprudence or indigenous knowledge could inform responsible data collection in the Middle East?
Islamic ethics and jurisprudence around principles like privacy (istikhfaf), consent (ridha), and prohibitions against prying (tajassus) into others’ personal lives could provide much-needed guardrails for responsible data practices. Scholars could draw from the Islamic legal tradition of developing clear data handling protocols to prevent exploitation and unwanted surveillance of Muslim communities.
Additionally, indigenous Middle Eastern knowledge systems like Avicennian logic, Muqarnas geometries, and Jali designs could offer novel frameworks for conceptualizing data collection, consent flows, and information architectures. Engaging with ethics embedded in these traditions could yield more culturally-resonant and trustworthy data governance models compared to opaque, extractive frameworks.
How can theories of “situated knowledges” help analyze cultural biases and omissions in mainstream datasets representing the region?
Donna Haraway’s notion of “situated knowledges” posits that all knowledge is inherently partial, embodied, and localized within particular perspectives. Examining mainstream datasets representing the Middle East through this lens reveals how they are situated within Western empiricist epistemologies that strip data of its socio-cultural contexts.
For instance, many image datasets canonize an orientalist visual vocabulary reducing the region to desert landscapes, mosques and camels. This reflects the situated gaze and biases of the predominantly Western male technologists who compiled the datasets. Theories of situated knowledges could recover other cultural perspectives overwritten by such datasets – perspectives shaped by gender, ethnicity, religion, and localized meaning systems across the Middle East’s diversity.
What counternarratives could regional multimedia data sources like Arab cinema or hip-hop offer to combat stereotypical AI training data?
Multimedia sources emerging from the region itself could serve as rich counter-datasets challenging AI systems’ stereotypical representations. The Arab cinema canon, for instance, offers more complex, grounded and self-representative narratives that could contextualize understanding of gender, migration, urban life and more.
The vivacity of regional hip-hop, rooted in minoritized youth subcultures from Gaza to Iran, could yield valuable data attentive to intersectional identities, dialectics, and socioeconomic realities typically flattened in text corpora. Consulting such counternarratives could enrich AI training data with pluralities and granularities missing from monolithic datasets.
How could consulting data genealogies excavate colonial influences on categorical systems used to label Middle Eastern data today?
Rigorously mapping the data genealogies and sociohistorical lineages underpinning modern datasets could unearth how colonial epistemologies continue shaping categorical systems used to label and classify Middle Eastern data.
For example, tracing image dataset ontologies to early Orientalist photographic archives could reveal interconnections between colonial visual regimes and contemporary computer vision datasets. Similarly for text corpora, conducting data archeology could uncover genealogical ties to taxonomies conceived by colonial-era institutions and philologists fueled by linguistic imperialism.
Such genealogical work could destabilize the “ground truth” objectivity claims of mainstream datasets by foregrounding how their taxonomic roots remain sculpted by dehumanizing colonial logics of ordering Middle Eastern knowledges. This recontextualization could motivate more ethical, participatory and culturally-grounded modes of labeling data.
OpenEdition schlägt Ihnen vor, diesen Beitrag wie folgt zu zitieren:
Victoria Mummelthei (24. Mai 2024). ai and the Middle East, data and the Middle East. Keine Disziplin – No Discipline. Abgerufen am 21. April 2026 von https://doi.org/10.58079/13jqv

