In Seeing Like a State, James Scott describes how governments have tried to make their societies more legible in pursuit of their basic organizational mission—”to arrange the population in ways that simplified the classic state functions of taxation, conscription, and prevention of rebellion.”
These state simplifications, the basic givens of modern statecraft, were, I began to realize, rather like abridged maps. They did not successfully represent the actual activity of the society they depicted, nor were they intended to; they represented only that slice of it that interested the official observer. They were, moreover, not just maps. Rather, they were maps that, when allied with state power, would enable much of the reality they depicted to be remade.
Statistical forecasts of political events are a form of legibility, too—an abridged map—with all the potential benefits and issues Scott identifies. Most of the time, the forecasts we generate focus on events or processes of concern to national governments and other already-powerful entities, like multinational firms and capital funds. These organizations are the ones who can afford to invest in such work, who stand to benefit most from it, and who won’t get in trouble for doing so. We talk about events “of interest” or “of concern” but rarely ask ourselves out loud: “Of interest to whom?” Sometimes we literally map our forecasts, but even when we don’t, the point of our work is usually to make the world more legible for organizations that are already wealthy or powerful so that they can better protect and expand their wealth and power.
If we’re doing our work as modelers right, then the algorithms we build to generate these forecasts will summarize our best ideas about things that cause or predict those events. Those ideas do not emerge in a vacuum. Instead, they are part of a larger intellectual and informational ecosystem that is also shaped by those same powerful organizations. Ideology and ideation cannot be fully separated.
In political forecasting, it’s not uncommon to have something that we believe to be usefully predictive but can’t include in our models because we don’t have data that reliably describe it. These gaps are not arbitrary. Sometimes they reflect technical, bureaucratic, or conceptual barriers, but sometimes they don’t. For example, no model of civil conflict can be close to “true” without including information about foreign support for governments and their challengers, but a lot of that support is deliberately hidden. Some of the same organizations that ask us to predict accurately hide from us some of the information we need most to do that.
Some of us try to escape the moral consequences of serving powerful organizations whose actions we don’t always endorse by making our work available to the public. If we share the forecasts with everyone, our (my) thinking goes, then we aren’t serving a particular master. Instead, we are producing a public good, and public goods are inherently good—right?
There are two problems with that logic. First, most of the public doesn’t have the interest or capacity to act on those forecasts, so sharing the forecasts with them will usually have little effect on their behavior. Second, some of the states and organizations that consume our public forecasts will apply them to ends we don’t like. For example, a dictatorial regime might see a forecast that it is susceptible to a new wave of nonviolent protest and respond by repressing harder. So, the practical effects of broadcasting our work will usually be modest, and some of them could even be harmful.
I know all of this, and I continue to do the work I do because it challenges and interests me, it pays well, and, I believe, some of it can help people do good. Still, I think it’s important periodically to remind ourselves—myself—that there is no escape from the moral consequences of this work, only trade-offs.