Similarity comparisons of the form "Is object a more similar to b than to c?" form a useful foundation in several computer vision and machine learning applications. Unfortunately, an embedding of n points is only uniquely specified by n 3 triplets, making collecting every triplet an expensive task. In noticing this difficulty, other researchers investigated more intelligent triplet sampling techniques, but they do not study their effectiveness or their potential drawbacks. Although it is important to reduce the number of collected triplets to generate a good embedding, it is also important to understand how best to display a triplet collection task to the user to better respect the worker's human constraints. In this work, we explore an alternative method for collecting triplets and analyze its financial cost, collection speed, and worker happiness as a function of the final embedding quality. We propose best practices for creating cost effective human intelligence tasks for collecting triplets. We show that rather than changing the sampling algorithm, simple changes to the crowdsourcing UI can drastically decrease the cost of collecting similarity comparisons. Finally, we provide a food similarity dataset as well as the labels collected from crowd workers.

With a large amount of tasks of various types, requesters in crowdsourcing platforms often bundle tasks of different types into a single working session. This creates a task switching setting, where workers need to shift between different cognitive tasks. We design and conduct an experiment on Amazon Mechanical Turk to study how occasionally presented performance-contingent monetary rewards, referred as monetary interventions , affect worker performance in the task switching setting. We use two competing metrics to evaluate worker performance. When monetary interventions are placed on some tasks in a working session, our results show that worker performance on these tasks can be improved in both metrics. Moreover, worker performance on other tasks where monetary interventions are not placed is also affected: workers perform better according to one metric, but worse according to the other metric. This suggests that in addition to providing extrinsic monetary incentives for some tasks, monetary interventions implicitly set performance goals for all tasks. Furthermore, monetary interventions are most effective in improving worker performance when used at switch tasks, tasks that follow a task of a different type, in working sessions with a low task switching frequency.

Personalization is a way for computers to support people’s diverse interests and needs by providing content tailored to the individual. While strides have been made in algorithmic approaches to personalization, most require access to a significant amount of data. However, even when data is limited online crowds can be used to infer an individual’s personal preferences. Aided by the diversity of tastes among online crowds and their ability to understand others, we show that crowdsourcing is an effective on-demand tool for personalization. Unlike typical crowdsourcing approaches that seek a ground truth, we present and evaluate two crowdsourcing approaches designed to capture personal preferences. The first, taste-matching , identifies workers with similar taste to the requester and uses their taste to infer the requester’s taste. The second, taste-grokking , asks workers to explicitly predict the requester’s taste based on training examples. These techniques are evaluated on two subjective tasks, personalized image recommendation and tailored textual summaries. Taste-matching and taste-grokking both show improvement over the use of generic workers, and have different benefits and drawbacks depending on the complexity of the task and the variability of the taste space.

We present theoretical and empirical results demonstrating the usefulness of social choice functions in crowdsourcing for participatory democracies. First, we demonstrate the scalability of social choice functions by defining a natural notion of epsilon-approximation, and giving algorithms which efficiently elicit such approximations for two prominent social choice functions: the Borda rule and the Condorcet winner. This result circumvents previous prohibitive lower bounds and is surprisingly strong: even if the number of ideas is as large as the number of participants, each participant will only have to make a logarithmic number of comparisons, an exponential improvement over the linear number of comparisons previously needed. Second, we apply these ideas to Finland's recent off-road traffic law reform, an experiment on participatory democracy in real life. This allows us to verify the scaling predicted in our theory and show that the constant involved is also not large. In addition, by collecting data on the time that users take to complete rankings of varying sizes, we observe that eliciting partial rankings can further decrease elicitation time as compared to the common method of eliciting pairwise comparisons.

While temporal behavioral patterns can be discerned to underlie real crowd work, prior studies have typically modeled worker performance under a simplified i.i.d. assumption. To better model such temporal worker behavior, we propose a time-series label prediction model for crowd work. This latent variable model captures and summarizes past worker behavior, enabling us to better predict the quality of each worker's next label. Given inherent uncertainty in prediction, we also investigate a decision reject option to balance the tradeoff between prediction accuracy vs. coverage. Results show our model improves accuracy of both label prediction on real crowd worker data, as well as data quality overall.

In online labor platforms such as Amazon Mechanical Turk, a good strategy to obtain quality answers is to take aggregate answers submitted by multiple workers, exploiting the wisdom of the crowd. However, human computation is susceptible to systematic biases which cannot be corrected by using multiple workers. We investigate a game-theoretic bonus scheme, called Peer Truth Serum (PTS), to overcome this problem. We report on the design and outcomes of a set of experiments to validate this scheme. Results show Peer Truth Serum can indeed correct the biases and increase the answer accuracy by up to 80%.

Retaining workers on micro-task crowdsourcing platforms is essential in order to guarantee the timely completion of batches of Human Intelligence Tasks (HITs). Worker retention is also a necessary condition for the introduction of SLAs on crowdsourcing platforms. In this paper, we introduce novel pricing schemes aimed at improving the retention rate of workers working on long batches of similar tasks. We show how increasing or decreasing the monetary reward over time influences the number of tasks a worker is willing to complete in a batch, as well as how it influences the overall latency. We compare our new pricing schemes against traditional pricing methods (e.g., constant reward for all the HITs in a batch) and empirically show how certain schemes effectively function as an incentive for workers to keep working longer on a given batch of HITs. Our experimental results show that the best pricing scheme in terms of worker retention is based on punctual bonuses paid whenever the workers reach predefined milestones.

Crowdsourcing and citizen science have established themselves in the mainstream of research methodology in recent years, employing a variety of methods to solve problems using human computation. An approach described here, termed "groupsourcing", uses social networks to present problems and collect solutions. This paper details a method for archiving social network messages and investigates messages containing an image classification task in the domain of marine biology. In comparison to other methods, groupsourcing offers a high accuracy, data-driven and low cost approach.

An ideal crowdsourcing or citizen-science system would route tasks to the most appropriate workers, but the best assignment is unclear because workers have varying skill, tasks have varying difficulty, and assigning several workers to a single task may significantly improve output quality. This paper defines a space of task routing problems, proves that even the simplest is NP-hard, and develops several approximation algorithms for parallel routing problems. We show that an intuitive class of requesters' utility functions is submodular, which lets us provide iterative methods for dynamically allocating batches of tasks that make near-optimal use of available workers in each round. Experiments with live oDesk workers show that our task routing algorithm uses only 48% of the human labor compared to the commonly used round-robin strategy. Further, we provide versions of our task routing algorithm which enable it to scale to large numbers of workers and questions and to handle workers with variable response times while still providing significant benefit over common baselines.

In this paper, an ensemble-based method for the screening of diabetic retinopathy (DR) is proposed. This approach is based on features extracted from the output of several retinal image processing algorithms, such as image-level (quality assessment, pre-screening, AM/FM), lesion-specific (microaneurysms, exudates) and anatomical (macula, optic disc) components. The actual decision about the presence of the disease is then made by an ensemble of machine learning classifiers. We have tested our approach on the publicly available Messidor database, where 90% sensitivity, 91% specificity and 90% accuracy and 0.989 AUC are achieved in a disease/no-disease setting. These results are highly competitive in this field and suggest that retinal image processing is a valid approach for automatic DR screening.