Thinking AI from the Global South

Thinking AI from the Global South: a critical itinerary

The resource map and its extension assemble a technical itinerary produced almost entirely in a handful of universities and companies in the global North. That does not invalidate them —by a wide margin, it is the best free material available— but it does determine what gets taught as “the problem”: how to improve accuracy, how to scale training, how to deploy to production. Almost never: with what data, extracted from whom, with what energy and what water, under what labour regime, and who ends up in a position to decide about any of it.

This entry assembles the other half of the itinerary. It is not “the ethics part” at the end of the course: it is a set of conceptual tools that change what you see when you look at a model. And it is organised with an explicit bias —Latin America and the Global South— not as a victim category or an audience to consult, but as a site where concrete institutional alternatives are already being produced.

Frameworks for seeing AI as infrastructure, not as algorithm

Crawford, K. (2021). Atlas of AI — Follows AI backwards, from the interface to lithium, water, labelling labour and data archives: the best entry point for moving from thinking about “algorithms” to thinking about planetary supply chains.
Calculating Empires (Kate Crawford and Vladan Joler) — A navigable, free visual genealogy of five centuries of technologies of calculation, communication, classification and control. Useful both for study and for teaching: a single enormous image where you can see that none of this began in 2022.
Couldry, N. and Mejias, U. (2019). The Costs of Connection — The most influential formulation of the data colonialism thesis: not a metaphor about colonialism, but the claim that a new appropriation —that of social life converted into data— has a structure analogous to the colonial appropriation of land and labour. Their research network, Tierra Común, publishes and organises largely from Latin America.
Birhane, A. (2020). Algorithmic Colonization of Africa — Short, forceful and open access. It shows how promises to “solve African problems with AI” reproduce infrastructural dependency and a model in which the continent supplies data and market and receives solutions designed elsewhere. Probably the single best text for understanding what is meant by “algorithmic colonialism”.
Mohamed, S., Png, M.-T. and Isaac, W. (2020). Decolonial AI — Written from inside the industry, it translates decolonial theory into concrete tactics for AI research practice: reverse design, caution about “beta-testing” on vulnerable populations, critical solidarity.
Ricaurte, P. (2019). Data Epistemologies, The Coloniality of Power, and Resistance — From Mexico, it articulates data extraction with the coloniality of power and knowledge and —most interestingly— with the practices of resistance that already exist in the region.
Mhlambi, S. (2020). From Rationality to Relationality: Ubuntu as an Ethical and Human Rights Framework for AI Governance — Proposes replacing the individual rational subject underlying almost all AI ethics with a relational ontology (ubuntu). It is the clearest example that “epistemologies of the South” does not mean applying the same frameworks with different examples, but changing the frameworks.
AI Decolonial Manyfesto — A short, collective, multilingual text. Useful for opening a discussion in a class or a team.

Algorithmic justice, bias and its limits

Algorithmic Justice League — Founded by Joy Buolamwini. Beyond the research, it has material designed for action: don’t miss her TED talk and the documentary Coded Bias, available on several platforms.
DAIR Institute — The independent research institute founded by Timnit Gebru after her departure from Google. AI research done explicitly outside the agenda of the large companies, with teams distributed across several continents.
Fairness and Machine Learning — The open book by Barocas, Hardt and Narayanan. Technical rigour about what can and cannot be measured as “bias”; its final chapter is a good vaccine against the idea that fairness is an optimisation problem.
Practical Data Ethics (Rachel Thomas, fast.ai) — The ethics course written by someone who also teaches the technical side; especially good on disinformation and on the feedback mechanisms of recommender systems.
Tech Ethics Curricula: A Collection of Syllabi (Casey Fiesler) — Hundreds of tech ethics syllabi in an open spreadsheet. If you have to build a course, start here.

Political economy and philosophy of technology

Zuboff, S. (2019). The Age of Surveillance Capitalism — The obligatory, if contested, reference: behavioural surplus as raw material. For a quick way in, there is Nick Rabb’s synthesis and its video version.
Frenken, K. — The rise of the platform economy — A lecture from before the enthusiasm cycle of 2022 onwards, and useful precisely for that: the analysis of the platform economy holds up without the noise of the present.
Floridi, L. (2023). The Ethics of Artificial Intelligence — The best summary (and position-taking) of the literature accumulated since around 2014, when much of the social problematic of AI began to settle around the question of ethics and good practice. Useful precisely for understanding the framework that the texts in the previous section argue with.
Coeckelbergh, M. (2020). AI Ethics (MIT Press, Essential Knowledge series) — Brief, orderly and a good map of the philosophical debate.
Frischmann, B. and Selinger, E. (2018). Re-Engineering Humanity — The inverted question: not whether machines become human, but to what extent the design of technical environments makes us more machine-like.
On techno-optimism and techno-pessimism — Marc Andreessen’s techno-optimist manifesto is worth reading as a document: not for the quality of its argument, but because it lays out with unusual frankness the ideology of much of the venture capital funding this field. Read it alongside Gómez, R. J. (1997), Progreso, determinismo y pesimismo tecnológico (Redes, 4(10)), which dismantles the false dilemma from within Argentine philosophy of science.

Videos to think with (and to teach with)

Zeynep Tufekci on why machine intelligence makes human morals more important, not less:

Sasha Luccioni on the concrete, present harms —environmental cost, bias, invisible labour— as against speculative risks:

Where this is being thought in Latin America

Research groups and organisations

Grupo GIFT — A research group in philosophy of technology; it organises annual workshops on AI, several of them at the Argentine Society for Philosophical Analysis (SADAF).
Laia — Laboratorio Abierto de Inteligencia Artificial — An Argentine open working space on AI.
Fundación Vía Libre — Sustained work from Córdoba on digital rights, free software and, in recent years, bias and discrimination in language models, with tools designed so that non-technical communities can audit stereotypes in models.
Derechos Digitales — A regional organisation (based in Chile) producing some of the best Spanish-language research on AI in the state, surveillance and human rights in Latin America.
Coding Rights — From Brazil, it crosses technology, gender and rights with a design and public-intervention approach.
LAVITS — The Latin American Network of Surveillance, Technology and Society Studies: conferences, dossiers and a consolidated regional academic community.
CENIA — Chile’s National Center for Artificial Intelligence; among other things, it coordinates the Latin American Artificial Intelligence Index (ILIA) and the effort to train regional language models with Latin American data and participation.
Masakhane — Not Latin American, but the most cited example of what this chapter discusses: a pan-African, distributed, grassroots community building NLP for African languages without waiting for a company in the North to decide it is worthwhile.

Curricular spaces

Regional technical community

Khipu — The Latin American meeting in artificial intelligence: an intensive research school that rotates among cities in the region.
LatinX in AI — A network and mentoring programme; it organises workshops at the field’s major conferences.
SciELO and AmeliCA — The open access publishing infrastructure built in the region, without article processing charges. They belong here for a precise reason: they are proof that Latin America has already built epistemic commons at continental scale, and therefore that the question of a public, regional AI infrastructure is not utopian but budgetary and political.

Events: 2nd AI Conference at Universidad Nacional de Córdoba (December 2024)

Full programme on the conference site (in Spanish).

Day 1

Day 2

Route D, in order

If you want an itinerary rather than a catalogue:

See the terrain: Crawford’s Atlas of AI, with Calculating Empires open alongside.
Name the structure: Birhane, Algorithmic Colonization of Africa → Mohamed, Png and Isaac, Decolonial AI → Couldry and Mejias.
Change the framework, not just the examples: Mhlambi (ubuntu), Ricaurte, Santos.
Land on what can be measured: fairmlbook and Practical Data Ethics, so as not to be left with critique and no tools.
Find where it is already being done: Vía Libre, Derechos Digitales, Masakhane, SciELO/AmeliCA. This step matters: without it, critique becomes an elegant description of a defeat.

And if you are coming from the technical side, the reverse order works just as well: start at step 5 —with a concrete organisation doing something— and work back up to the frameworks.

So far the chapter has worked on AI in general: what it is, how to learn it, who produces it and at whose expense. But the same technology changes shape depending on where it lands, and the questions become different ones when there is a classroom, a hospital or a public office on the other side. That is the next chapter.

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