The limited and highly variable resource dynamics of underserved communities, each with their own unique needs and values, underscore the need to integrate a context-aware approach when designing for these settings. Context-aware computing has long been a fundamental aspect of ubiquitous and pervasive systems, yet its application in Information and Communication Technologies for Development (ICT4D) remains limited. Existing context-aware approaches are predominantly designed for resource-rich environments and privileged communities, often failing to account for the unique constraints and dynamics of underserved populations. In this paper, we advocate for a paradigm shift in ICT system and service design to serve not only the privileged but also the underserved. Through the lens of two real-world case studies, we illustrate the contextual challenges faced by underserved communities and validate the design goals of our proposed framework by grounding them in real-world constraints, needs, and potential outcomes. Drawing upon existing literature and insights from the case studies, we first redefine context in ICT4D as a dynamic interplay of situated location, community needs, and limited resources, emphasizing a community-centered perspective. Building upon this definition, we conceptualize a more community-context-aware ICT4D design and propose enabling technologies for integrating community-in-the-loop methodologies, efficient resource allocation mechanisms, and context-aware service resiliency and adaptability strategies to enhance ICT services in resource-limited settings. By introducing a more context-aware approach to ICT4D, this paper aims to foster inclusivity, mitigate information inequity, and contribute to bridging the digital divide. Our work lays the foundation for future research on inclusive, resource-efficient, and community-driven context-aware ICT solutions.
As AI systems consume an increasing share of global data center capacity, their environmental and social impacts are growing rapidly. Data centers’ impacts, ranging from energy and water consumption to labour exploitation and land displacement, are obscured by Big Tech developing the models. However, emerging research and advocacy surface the connections between data centers and the ecological and social burdens they generate. This workshop supports and builds on these ongoing conversations through two interactive components. First, we ground AI in place by inviting participants to locate and reflect on the infrastructures, specifically data centers, that underpin algorithmic systems in their own communities. This exercise helps surface the material realities and localized impacts of developing AI models. Second, we explore how humour, satire, and visual arts can be used to reveal data centers’ social and environmental impacts, challenge norms, and connect people, especially when conversations around the climate crisis can often lead to despair and anxiety. We believe creative engagement offers a unique avenue for connection, reflection, critique, and hope. Through this workshop, we aim to foster collective learning, spark a sense of collective agency, and grow the global network of communities attuned to the place-based social and environmental impacts of data centers. Together, we develop visual materials that the community can use, and contribute to community building in the face of the issues of accountability, transparency, and ecological and social harms that result from a rampant scaling of AI.
Artificial intelligence is facing a crisis. Humans are consuming far too many resources, impeding the progress of larger, faster, newer models. It’s time to drink less, shower less, and to prioritize AI. Recent developments in AI technologies have relied on scaling technical infrastructure and capital. While improvements in benchmark performance have been put under the spotlight, environmental and social harms are obfuscated by Big Tech developing the models. Through an art-based initiative, titled Save the AI, we lean into satire and absurdity to visceralize AI’s staggering environmental and social impact. We designed posters, stickers, and postcards to be placed in public spaces to connect to people’s basic needs for resources that are fundamental to their survival and AI’s increasing demand for these same resources. We aim to spark curiosity, reflection, and conversation through our physical designs by leading people to an online networking campaign. This campaign helps people from different parts of the world connect, collectively express their concerns, and work in solidarity.
Geospatial Copilots hold immense potential for automating Earth observation (EO) and climate monitoring workflows, yet their reliance on large-scale models such as GPT-4o introduces a paradox: tools intended for sustainability studies often incur unsustainable costs. Using agentic AI frameworks in geospatial applications can amass thousands of dollars in API charges or requires expensive, powerintensive GPUs for deployment, creating barriers for researchers, policymakers, and NGOs. Unfortunately, when geospatial Copilots are deployed with open language models (OLMs), performance often degrades due to their dependence on GPT-optimized logic. In this paper, we present Geo-OLM, a tool-augmented geospatial agent that leverages the novel paradigm of state-driven LLM reasoning to decouple task progression from tool calling. By alleviating the workflow reasoning burden, our approach enables low-resource OLMs to complete geospatial tasks more effectively. When downsizing to small models below 7B parameters, Geo-OLM outperforms the strongest prior geospatial baselines by 32.8% in successful query completion rates. Our method performs comparably to proprietary models achieving results within 10% of GPT-4o, while reducing inference costs by two orders of magnitude from $500-$1000 to under $10. We present an in-depth analysis with geospatial downstream benchmarks, providing key insights to help practitioners effectively deploy OLMs for EO applications.
Artificial intelligence (AI) computing and data centers consume large amounts of freshwater, both directly for cooling and indirectly for electricity generation. While most attention has been paid to developed countries such as the U.S., this paper presents the first-of-its-kind dataset that combines nation-level weather and electricity generation data to estimate water usage efficiency for data centers in 41 African countries across five different climate regions. We also use our dataset to evaluate and estimate the water consumption of inference on two large language models (i.e., Llama-3-70B and GPT-4) in 11 selected African countries. Our estimates suggest that writing a 10-page report using Llama-3-70B could consume as much as 0.6 liters of water, while the water consumption by GPT-4 for the same task may go up to about 53 liters. For writing a medium-length email of 120-200 words, Llama-3-70B and GPT-4 could consume about 0.12 liters and 2.6 liters of water, respectively. Interestingly, given the same AI model, 8 out of the 11 selected African countries consume less water than the global average, mainly because of lower water intensities for electricity generation. However, water consumption can be substantially higher in some African countries with a steppe climate than the U.S. and global averages, prompting more attention when deploying AI computing in these countries. Our dataset is publicly available.1
