Resistance as Feedback
Every technology transition produces resistance, and every dominant discourse interprets that resistance as noise. The Luddites were backward. The farmers who rejected Green Revolution seeds were irrational. The communities that refused structural adjustment were obstacles to progress. In each case, the interpretive framework available to the dominant discourse contained only two categories for the response of those affected by the intervention: adoption (rational, progressive) or refusal (irrational, regressive, evidence of the population's need for further intervention). The possibility that refusal might constitute a form of knowledge — a diagnostic assessment of the intervention's inadequacy, grounded in experience that the interveners do not possess — does not arise within the framework, because the framework does not contain the category that would make it visible.
In the AI Story
Escobar's framework provides that category. Resistance, in his analysis, is not the opposite of progress. It is counter-expertise — a body of knowledge about the conditions under which interventions produce harmful effects, accumulated through the experience of communities subjected to multiple rounds of intervention over decades or centuries. The knowledge is practical rather than theoretical, embodied in decisions rather than articulated in position papers, validated by outcomes rather than by peer review. But it is knowledge, and its systematic exclusion from the discourse about AI constitutes a loss for the broader project of understanding how AI tools should be designed.
The farmer who declines to use an AI-powered agricultural advisory system because the system does not account for her soil's specific microbial ecology, her community's rotational planting calendar, or the relationship between crop selection and watershed management is not being technophobic. She is exercising diagnostic function: evaluating the technology not by its intrinsic capability but by its relationship to the conditions of her life. Her evaluation may be wrong — the AI system may offer genuine improvements her assessment does not capture. But her evaluation may also be right in ways the system's designers cannot see, because her knowledge of the local ecology is more granular, more temporally deep, and more relationally complex than anything the training data contains.
Escobar distinguishes between reactive resistance and what he calls the politics of refusal — a generative practice that establishes boundaries around the terms of engagement without rejecting engagement altogether. The Zapatista communities in Chiapas practice the politics of refusal when they use mobile phones for communication while declining to participate in the digital platforms the phones enable access to. They have not rejected the technology. They have established community-defined terms under which the technology is deployed. Indigenous communities in the Amazon who document traditional knowledge using digital recording tools while refusing to upload that documentation to external databases practice a version of the same politics.
The framing of resistance as friction is itself a political act. It positions the technology as the standard against which human behavior is evaluated: adoption is normal, refusal is deviant. Escobar's framework reverses the framing. Human purposes are the standard against which the technology is evaluated: adequacy is demonstrated when the tool serves purposes defined by the community. The reversal produces different prescriptions. If resistance is friction, the prescription is more education, demonstration, and subsidy. If resistance is feedback, the prescription is redesign — modifications to the tool, to the terms of its deployment, or to the institutional arrangements that govern its distribution.
Origin
The concept draws on the broader tradition of analyzing resistance in postcolonial theory and social movement studies, particularly James Scott's Weapons of the Weak and Seeing Like a State.
Escobar has developed the framework through his ethnographic work with Afro-Colombian and indigenous communities, where the distinction between reactive refusal and generative politics of refusal emerged from observing actual community practice.
Key Ideas
Counter-expertise. Resistance contains knowledge about the conditions under which interventions produce harm, accumulated through historical experience.
Diagnostic, not backward. The community that declines an AI tool is exercising the same evaluative function the technology industry claims to value: identifying failure modes.
Politics of refusal. Generative refusal establishes terms of engagement rather than rejecting engagement altogether.
Selective adoption. Communities can adopt capabilities while declining platforms, distinguishing analytically between what the technology enables and what it demands.
Institutional implications. Treating resistance as feedback requires institutions capable of incorporating community evaluation into technology governance.
Appears in the Orange Pill Cycle
Further reading
- James C. Scott, Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed (Yale University Press, 1998).
- Arturo Escobar, Territories of Difference (Duke University Press, 2008).
- Raúl Zibechi, Territories in Resistance (AK Press, 2012).
- Marisol de la Cadena, Earth Beings (Duke University Press, 2015).