Two guiding questions of this workshop are:
What different types of costs of AI are there?
How can research communities meaningfully engage with AI-related costs?
Topics of interest include, but are not limited to:
Human labor in AI production and use: the development and use of AI technologies require large-scale human labor, such as in data generation [2, 11], verification of automated outputs [17], and maintenance [3], leading to the extraction of data and labor globally [4, 11].
What types of labor are integral to AI pipelines?
What are their contexts, conditions, and characteristics?
What types of costs arise from these labor practices?
Infrastructures of computing: Centering infrastructures of computing recognizes the materiality of computation and AI, including cables [15], data centers [6], and cloud infrastructures [12], and reveals the implications of tech giants’ dominance and control over critical AI infrastructure, such as economic centralization, smaller companies’ structural dependency on these infrastructures, and the potential of homogenization in knowledge production [1, 10].
What are the consequences of the increasing and structural dependence on AI-related infrastructure controlled by tech giants?
Environmental costs of AI: Despite being presented as a solution to combat climate changes, current AI systems are resource-intensive and have considerable environmental impacts regarding energy consumption [16], carbon emissions [13], water usage [9], lithium extraction and broader transformation of territory into resources and assets [7]. This raises an open question whether using AI technologies to combat climate catastrophe will create a larger environmental footprint overall [14].
How can we measure, quantify, track, and visualize the environmental impacts the AI sector brings?
How can we conceive of climate justice given the uneven distribution of benefits and environmental impacts in AI development and use?
Alternative methods to engage with AI-related costs: Considering broader AI-related costs shows pathways to alternative methodologies, such as community-driven approaches [8], asset-based approaches [18] and action research [5]. These methodologies call for fostering changes that are grounded in social contexts and in networks of communities and stakeholders, such as building communities’ capacities, providing infrastructures and resources, and facilitating them to gain more agency.
What are the theoretical frameworks, methodologies, and empirical cases to strengthen negotiation, resistance, and re-imagining of AI costs and futures?
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