No Data in the Void: Values and Distributional Conflicts in Empirical Policy Research and Artificial Intelligence Economics for Inclusive Prosperity

Economics has experienced an empirical turn in the last few decades. We have entered an era of big data, machine learning, and artificial intelligence. Experimental methods have greatly increased in importance in both the social and life sciences. And recent efforts at reforming the publication system promise to improve the replicability and credibility of published findings. One might be tempted to conclude that this increased availability of and reliance on quantitative evidence allows us to dispense with the normative judgements of earlier days. I will argue that the opposite is the case. The choice of objective functions, which define our goals, and of the set of policies to be considered matters ever more in all of these contexts. A famous example in debates about the dangers of artificial intelligence (AI) is the hypothetical AI system with the objective of producing as many paperclips as possible. If sufficiently capable, such an AI system might end up annihilating humanity in the pursuit of this objective. Another example is the design of experiments. The majority of experiments in the social and life sciences are designed based on the (implicit) objective of obtaining precise estimates of causal effects. Such experiments randomly assign treatments using fixed probabilities.