The successful application of artificial intelligence depends on a set of preconditions. Some are obvious. For example, to be successful AI needs to be able to access some digital representation of its environment, either through sensors mapping the world or through the input of existing data. Where these representations are difficult to come by or data are scarce, as in many areas of politics, AI will not be successful. Other preconditions are not so obvious. For example, for AI to produce helpful results, the underlying connections between inputs and outputs must be stable over time. This points to two problems: unobserved temporal shifts between variables and the dangers of relying on purely correlative evidence without support of causal models.

More important still, especially with respect to democracy, is that normatively speaking the past must provide a useful template for the future. Change is a crucial feature of societies, especially the extension of rights and the participation of previously excluded groups. Over time, many societies strive to decrease discrimination and increase equality. In fact, many policies are consciously designed to break with past patterns of discrimination. AI-based predictions and classifications based on past patterns risk replicating systemic inequalities and even structural discrimination.

Few problems in politics and in democracy more broadly share these characteristics. This limits the application of AI in society and, accordingly, its impact on democracy.

Skript: http://digitalmedia.andreasjungherr.de/ai.html#conditions-for-the-successful-application-of-artificial-intelligence