Navigating Universal Representation
The Minimum Information Representation Algorithm (MIRA) is a principle stating that the visual representation of universal concepts inevitably results in a loss of information and introduction of bias. As the universality of a concept increases, the difficulty of creating an inclusive and comprehensive visual representation increases proportionally, leading to unavoidable exclusions or misrepresentations of certain perspectives or groups.
We acknowledge the inherent challenges in visually representing broad, universal concepts as described by the Minimum Information Representation Algorithm (MIRA). In our work, we strive to include diverse perspectives and are acutely aware of the sensitivities surrounding visual representation. We engage in thoughtful consideration of the potential impacts and repercussions of our visual choices.
However, we recognize that compressing general ideas into specific visual elements has inherent limitations. The MIRA principle underscores that even with the utmost care and intention, some degree of exclusion or bias may be unavoidable when visually depicting universal concepts. We present our visual work with this understanding, inviting dialogue and continual improvement in our quest for more inclusive and representative visual communication.
Reimagining Earth
We acknowledge the Eurocentric biases in standard cartographic conventions, such as Greenwich-centered projections and North-up orientations. These norms subtly influence global power perceptions and distort geographical relationships. Our work explores alternative representations, including South-up maps and centering traditionally marginalized regions like New Zealand. By challenging cartographic assumptions, we aim to foster a more inclusive and nuanced understanding of global geography, encouraging viewers to reconsider their perceptions of Earth’s spatial relationships and geopolitical dynamics.
Our Use of AI
Most of the writing on this site is drafted with the help of large language models, as is much of the code behind its interactive visualisations and much of the work of reshaping the datasets they draw on.
What the tools do not supply is substance. Every figure, curve and date traces to a named source — peer-reviewed literature where it exists, published institutional datasets otherwise. Where a source is silent we say so rather than fill the gap. Nothing is estimated or invented to make a visualisation look finished, nothing is published that a person has not checked, and no claim on this site originates with a machine.
That being said, we are not comfortable with this. The energy and water the datacentre industry draws are a planetary matter: we map that infrastructure on Existential Risks, trace it from quartz seam to seabed cable in Machine Intelligence Infrastructure, and follow the field’s own dangers in the AI Risks Observatory. The models were trained on work whose authors were neither asked nor credited. We give our own images away for educational use and ask two things in return — attribution, and no reworking without consent — which are exactly the conditions that kind of training sets aside. The question is not an abstract one for us. And several prominent voices in the field hold views about humanity’s future that we set our distance from.
We use these tools anyway, because a very small organisation cannot otherwise attempt work at this scale, and declining them would reduce none of those harms — only the number of attempts to make planetary science legible. That judgement is provisional. If it changes, we will say so here. Anyone who wants no part of these tools has our respect.
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