Question answering systems are rapidly advancing, but their opaque nature may impact user trust. We explored trust through an anti-monitoring framework, where trust is predicted to be correlated with presence of citations and inversely related to checking citations. We tested this hypothesis with a live question-answering experiment that presented text responses generated using a commercial Chatbot along with varying citations (zero, one, or five), both relevant and random, and recorded if participants checked the citations and their self-reported trust in the generated responses. We found a significant increase in trust when citations were present, a result that held true even when the citations were random; we also found a significant decrease in trust when participants checked the citations. These results highlight the importance of citations in enhancing trust in AI-generated content.

The discovery and analysis of network patterns are central to the scientific enterprise. In the present work, we developed and evaluated a new approach that learns the building blocks of graphs that can be used to understand and generate new realistic graphs. Our key insight is that a graph's clique tree encodes robust and precise information. We show that a Hyperedge Replacement Grammar (HRG) can be extracted from the clique tree, and we develop a fixed-size graph generation algorithm that can be used to produce new graphs of a specified size. In experiments on large real-world graphs, we show that graphs generated from the HRG approach exhibit a diverse range of properties that are similar to those found in the original networks. In addition to graph properties like degree or eigenvector centrality, what a graph "looks like" ultimately depends on small details in local graph substructures that are difficult to define at a global level. We show that the HRG model can also preserve these local substructures when generating new graphs.

Discovering the underlying structures present in large real world graphs is a fundamental scientific problem. In this paper we show that a graph's clique tree can be used to extract a hyperedge replacement grammar. If we store an ordering from the extraction process, the extracted graph grammar is guaranteed to generate an isomorphic copy of the original graph. Or, a stochastic application of the graph grammar rules can be used to quickly create random graphs. In experiments on large real world networks, we show that random graphs, generated from extracted graph grammars, exhibit a wide range of properties that are very similar to the original graphs. In addition to graph properties like degree or eigenvector centrality, what a graph ``looks like'' ultimately depends on small details in local graph substructures that are difficult to define at a global level. We show that our generative graph model is able to preserve these local substructures when generating new graphs and performs well on new and difficult tests of model robustness.

We are confronted with massive amounts of information at every turn. In order to efficiently reason about knowledge and information, humans have evolved efficient strategies for organizing complex concepts in order to form connections between and recall information. This behavior can be observed and codified when people search for objects within digital information networks. Current models of search behavior exhibit unnecessary or extraneous complexity. Minimal or simple modifications to well established algorithms yield valid models of human navigation by exploring hierarchical information inherent in networks. We explore and validate a new model of how humans navigate an information networks. To that end, we present a new path finding algorithm that approximates human navigation by leveraging the categorical classification of the nodes within the network. We compare our new model, CatPath, to existing graph distance measures when possible and show that the category paths are largely correlated with traces of human navigation.

There is much discussion in the robotics community concerning the nature of people's impressions of robots. This pilot study employed the use of mobile robots coupled with artistic elements to create an environment conducive to human participation. PhotoBot took photos of participants (n = 16) in a gallery space and provided them with a physical copy of their image, while ProjectorBot displayed 3D Kinect imagery for participants to view. Participants completed a self-report measure of rapport (Bernieri's Rapport Criterion); the results of which suggest that they experienced a high degree of positive interaction with the robots.