Drone images accumulate much faster than they can be analyzed. Researchers have developed a new approach that combines crowdsourcing and machine learning to speed up the process. Who would win in a real-life game of "Where's Waldo," humans or computers? A recent study suggests that when speed and accuracy are critical, an approach combing both human and machine intelligence would take the prize. With drones being used to monitor everything natural disaster sites, pollution, or wildlife populations, analyzing drone images in real-time has become a critically important big data challenge.
We examine designs for crowdsourcing contests, where participants compete for rewards given to superior solutions of a task. We theoretically analyze tradeoffs between the expectation and variance of the principal's utility (i.e. the best solution's quality), and empirically test our theoretical predictions using a controlled experiment on Amazon Mechanical Turk. Our evaluation method is also crowdsourcing based and relies on the peer prediction mechanism. Our theoretical analysis shows an expectation-variance tradeoff of the principal's utility in such contests through a Pareto efficient frontier. In particular, we show that the simple contest with 2 authors and the 2-pair contest have good theoretical properties. In contrast, our empirical results show that the 2-pair contest is the superior design among all designs tested, achieving the highest expectation and lowest variance of the principal's utility.
In the U.S., individuals give more than 200 billion dollars to over 50 thousand charities each year, yet how people make these choices is not well understood. In this study, we use data from CharityNavigator.org and web browsing data from Bing toolbar to understand charitable giving choices. Our main goal is to use data on charities' overhead expenses to better understand efficiency in the charity marketplace. A preliminary analysis indicates that the average donor is "wasting" more than 15% of their contribution by opting for poorly run organizations as opposed to higher rated charities in the same Charity Navigator categorical group. However, charities within these groups may not represent good substitutes for each other. We use text analysis to identify substitutes for charities based on their stated missions and validate these substitutes with crowd-sourced labels. Using these similarity scores, we simulate market outcomes using web browsing and revenue data. With more realistic similarity requirements, the estimated loss drops by 75%—much of what looked like inefficient giving can be explained by crowd-validated similarity requirements that are not fulfilled by most charities within the same category. A choice experiment helps us further investigate the extent to which a recommendation system could impact the market. The results indicate that money could be redirected away from the long-tail of inefficient organizations. If widely adopted, the savings would be in the billions of dollars, highlighting the role the web could have in shaping this important market.
Crowd-sourcing is a cheap and popular means of creating training and evaluation datasets for machine learning, however it poses the problem of `truth inference', as individual workers cannot be wholly trusted to provide reliable annotations. Research into models of annotation aggregation attempts to infer a latent `true' annotation, which has been shown to improve the utility of crowd-sourced data. However, existing techniques beat simple baselines only in low redundancy settings, where the number of annotations per instance is low ($\le 3$), or in situations where workers are unreliable and produce low quality annotations (e.g., through spamming, random, or adversarial behaviours.) As we show, datasets produced by crowd-sourcing are often not of this type: the data is highly redundantly annotated ($\ge 5$ annotations per instance), and the vast majority of workers produce high quality outputs. In these settings, the majority vote heuristic performs very well, and most truth inference models underperform this simple baseline. We propose a novel technique, based on a Bayesian graphical model with conjugate priors, and simple iterative expectation-maximisation inference. Our technique produces competitive performance to the state-of-the-art benchmark methods, and is the only method that significantly outperforms the majority vote heuristic at one-sided level 0.025, shown by significance tests. Moreover, our technique is simple, is implemented in only 50 lines of code, and trains in seconds.