Decolonizing AI? Lessons from a failed experiment - Martin Tironi; Camila Albornoz (Big Data & Society)
In recent years, multiple discourses have arisen about the necessity of decolonizing the imaginaries surrounding artificial intelligence, which tend to reinforce the interests and values of the Global North. A key challenge lies in unsettling these dominant imaginaries by developing conceptual and methodological tools that foster technodiversity and promote more inclusive approaches to technological development. This article reflects on a failed speculative design intervention that sought to decolonize artificial intelligence imaginaries, drawing on Latin American contexts as a point of reference. This experience of failure prompts us to question the nature and limitations of the critical apparatus that was generated. Although the intervention we described followed a problem-validating approach, we consider that its failure can be conceptualized as a call to cultivate a new sensitivity to problem-making experiments and encourage deeper engagement with the critical insights offered by the participants themselves. Hence, we conceptualized failure as an opportunity to interrogate metalanguages and expert knowledge, which ultimately silences the critical practices of individuals.