RESUMEN

As Artificial Intelligence (AI) proliferates across important social institutions, many of the most powerful AI systems available are difficult to interpret for end-users and engineers alike. Here, we sought to characterize public attitudes towards AI interpretability. Across seven studies (N = 2475), we demonstrate robust and positive attitudes towards interpretable AI among non-experts that generalize across a variety of real-world applications and follow predictable patterns. Participants value interpretability positively across different levels of AI autonomy and accuracy, and rate interpretability as more important for AI decisions involving high stakes and scarce resources. Crucially, when AI interpretability trades off against AI accuracy, participants prioritize accuracy over interpretability under the same conditions driving positive attitudes towards interpretability in the first place: amidst high stakes and scarce resources. These attitudes could drive a proliferation of AI systems making high-impact ethical decisions that are difficult to explain and understand.

Asunto(s)

Inteligencia Artificial , Opinión Pública , Actitud , Humanos

RESUMEN

Detecting where an epidemic started, i.e., which node in a network was the source, is of crucial importance in many contexts. However, finding the source of an epidemic can be challenging, especially because the information available is often sparse and noisy. We consider a setting in which we want to localize the source based exclusively on the information provided by a small number of observers - i.e., nodes that can reveal if and when they are infected - and we study where such observers should be placed. We show that the optimal observer placement depends not only on the topology of the network, but also on the variance of the node-to-node transmission delays. We consider both low-variance and high-variance regimes for the transmission delays and propose algorithms for observer placement in both cases. In the low-variance regime, it suffices to only consider the network-topology and to choose observers that, based on their distances to all other nodes in the network, can distinguish among possible sources. However, the high-variance regime requires a new approach in order to guarantee that the observed infection times are sufficiently informative about the location of the source and do not get masked by the noise in the transmission delays; this is accomplished by additionally ensuring that the observers are not placed too far apart. We validate our approaches with simulations on three real-world networks. Compared to state-of-the-art strategies for observer placement, our methods have a better performance in terms of source-localization accuracy for both the low- and the high-variance regimes.