TLDR: AI alignment, which ensures AI systems behave according to societal norms, often overlooks the significant geographic variations in what is considered appropriate, truthful, or legal. A new research paper introduces ‘geo-alignment,’ a framework to address this by making AI systems aware of and adaptable to specific local contexts. It highlights how geographic bias in data and models leads to misaligned outputs and proposes formalizing geo-alignment, along with research directions like creating geographic benchmarks, using neurosymbolic AI, and learning from spatial structures to build more culturally and legally sensitive AI.
Artificial intelligence systems are becoming increasingly integrated into our daily lives, mediating knowledge and shaping our understanding of reality. A core challenge in AI development is ‘alignment’ – ensuring these systems behave in accordance with societal norms, values, and goals. While much research focuses on preventing biases, addressing inequalities, and enhancing safety, a critical dimension has often been overlooked: the geographic variability of what is considered appropriate, truthful, or even legal.
A new research paper, “Whose Truth? Pluralistic Geo-Alignment for (Agentic) AI”, introduces the concept of ‘geo-alignment’ to address this gap. Authored by Krzysztof Janowicz, Zilong Liu, Gengchen Mai, Zhangyu Wang, Ivan Majic, Alexandra Fortacz, Grant McKenzie, and Song Gao, the paper argues that a one-size-fits-all approach to AI alignment is insufficient given the vast cultural, political, and legislative differences across regions.
The Problem with Universal Alignment
Imagine a text-to-image model that depicts a perfectly balanced gender ratio in company leadership, even though real-world statistics show imbalances. While this might seem like a positive step towards debiasing, it can create outputs that diverge from current realities. More critically, what is considered ‘truthful’ or ‘appropriate’ can vary significantly by location. For instance, statements about politically sensitive regions like Kashmir cannot be generated without understanding the user’s origin and current location, much like Google Maps displays different administrative borders based on where a user is accessing it from.
The paper highlights that existing AI alignment efforts often confuse inter-human variation with mere ‘noise.’ This becomes particularly problematic with the rise of ‘agentic AI’ – autonomous systems that will act in real geographic space. A recommendation for an over-the-counter medication in the US might be strictly regulated or even illegal in another country, or classified as a banned substance in sports contexts. Current AI systems often provide US-centric answers, failing to account for these crucial regional differences.
Geographic Bias and Trust in AI
The issue of geographic variability is deeply intertwined with bias. Many widely used datasets, such as ImageNet, exhibit strong geographic biases, with a disproportionate amount of data from developed Western countries. This leads to AI models performing poorly or exhibiting favoritism towards certain regions, or even showing bias against areas with lower socioeconomic conditions. For example, large language models have been shown to have geopolitical biases.
Attempts to debias AI models, while well-intentioned, can sometimes lead to new, unintended problems. The paper cites the example of Google’s Gemini depicting black and female Nazi soldiers due to overcorrective measures to increase ethnic and gender representation. Furthermore, efforts to reduce AI hallucination rates have, ironically, been linked to a decrease in the geographic diversity of AI chatbot outputs. This raises questions of trust: who decides on debiasing measures, and whose cultural norms act as a reference for alignment, especially when many models are developed by individuals from Western cultures and data labeling is often done in the Global South?
Formalizing Geo-Alignment
To move towards a solution, the researchers propose a formal definition for geo-alignment. An AI system is considered geo-aligned if, for any given query and geographic context, its output distribution closely resembles the locally appropriate distribution of outputs within an acceptable tolerance. This means the system needs to understand and adapt to the nuances of a specific region, rather than applying a generic global standard.
The challenge lies in estimating these ‘locally appropriate’ distributions, as regional boundaries can be vague, data might be sparse, and what’s acceptable can vary by query and use case. The paper uses the example of pseudoephedrine regulations to illustrate this complexity: regulations vary not just by country, but by state, transaction limits, and over time, making a simple list of ‘all reasonable answers’ impractical for an AI system designed for synthesis.
Future Directions for Research
The paper outlines several promising avenues for future research to achieve geo-alignment:
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Geographic Alignment Benchmarks
Developing benchmarks that focus on collective societal norms, values, and goals within specific regions. This would allow for evaluating how well AI systems align with local knowledge and customs, and identify how existing models might misrepresent local populations. Such benchmarks would also extend beyond traditional geographic features to include culturally sensitive concepts, like the color of a wedding dress, which varies significantly across cultures.
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Neurosymbolic Approaches to Geo-Alignment
Combining the strengths of symbolic AI (which can represent multiple, even contradictory, perspectives) with neural networks (which handle noise and implicit context well). Inspired by ‘deliberative alignment’ where AI models reason through safety specifications, this approach would involve teaching models to recognize when a query requires geo-alignment and then reason over spatially explicit declarative specifications (perhaps using geo-knowledge graphs) to produce aligned outputs.
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Learning from Spatial Structure
Leveraging natural spatial structures, such as spatial autocorrelation and topology, to align AI models. The idea is that nearby regions are more likely to have similar regulations and customs. By learning these spatial structures, AI models can reason about geographic context similarity, helping to overcome data sparsity issues and infer context for subregions within a larger area.
In conclusion, the vision of geo-alignment emphasizes that spatial structure is not just another dimension of pluralism, but a source of learnable patterns that can be leveraged for more effective AI alignment and evaluation. This will be crucial for the development of agentic AI systems that operate responsibly and appropriately in the diverse geographic realities of our world.
