The Workshop Program of the Association for the Advancement of Artificial Intelligence’s Sixth AAAI Conference on Human Computation and Crowdsourcing was held on the campus of the University of Zurich in Zurich, Switzerland on 5 July 2018. There were three full-day workshops in the program: CrowdBias: Disentangling the Relation between Crowdsourcing and Bias Management; Subjectivity, Ambiguity, and Disagreement in Crowdsourcing; Work in the Age of Intelligent Machines; a three-quarter day workshop, Advancing Human Computation with Complexity Science; and Project Networking; and a quarter day Project Networking workshop. This report contains summaries of three of the events.  

One of the key uses of causes is to explain why things happen. Explanations of specific events, like an individual's heart attack on Monday afternoon or a particular car accident, help assign responsibility and inform our future decisions. Computational methods for causal inference make use of the vast amounts of data collected by individuals to better understand their behavior and improve their health. However, most methods for explanation of specific events have provided theoretical approaches with limited applicability. In contrast we make two main contributions: an algorithm for explanation that calculates the strength of token causes, and an evaluation based on simulated data that enables objective comparison against prior methods and ground truth. We show that the approach finds the correct relationships in classic test cases (causal chains, common cause, and backup causation) and in a realistic scenario (explaining hyperglycemic episodes in a simulation of type 1 diabetes).

This paper proposes the use of the Brownian distance correlation to conduct a lead-lag analysis of financial and economic time series. When this methodology is applied to asset prices, the non-linear relationships identified may improve the price discovery process of these assets. The Brownian distance correlation determines relationships similar to those identified by the linear Granger causality test, and it also uncovers additional non-linear relationships among the log prices of oil, coal, and natural gas.

The way in which social conventions emerge in communities has been of interest to social scientists for decades. Here we report on the emergence of a particular social convention on Twitter—the way to indicate a tweet is being reposted and to attribute the content to its source. Initially, different variations were invented and spread through the Twitter network. The inventors and early adopters were well-connected, active, core members of the Twitter community. The diffusion networks of these conventions were dense and highly clustered, so no single user was critical to the adoption of the conventions. Despite being invented at different times and having different adoption rates, only two variations came to be widely adopted. In this paper we describe this process in detail, highlighting insights and raising questions about how social conventions emerge.