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Crowdsourcing information constitutes an important aspect of human-in-the-loop learning for researchers across multiple disciplines such as AI, HCI, and social science. While using crowdsourced data for subjective tasks is not new, eliciting useful insights from such data remains challenging due to a variety of factors such as difficulty of the task, personal prejudices of the human evaluators, lack of question clarity, etc. In this paper, we consider one such subjective evaluation task, namely that of estimating experienced emotions of distressed individuals who are conversing with a human listener in an online coaching platform. We explore strategies to aggregate the evaluators choices, and show that a simple voting consensus is as effective as an optimum aggregation method for the task considered. Intrigued by how an objective assessment would compare to the subjective evaluation of evaluators, we also designed a machine learning algorithm to perform the same task. Interestingly, we observed a machine learning algorithm that is not explicitly modeled to characterize evaluators subjectivity is as reliable as the human evaluation in terms of assessing the most dominant experienced emotions.
Image demosaicking and denoising are the two key fundamental steps in digital camera pipelines, aiming to reconstruct clean color images from noisy luminance readings. In this paper, we propose and study Wild-JDD, a novel learning framework for joint
Differential privacy is a promising framework for addressing the privacy concerns in sharing sensitive datasets for others to analyze. However differential privacy is a highly technical area and current deployments often require experts to write code
Large-scale labeled datasets are the indispensable fuel that ignites the AI revolution as we see today. Most such datasets are constructed using crowdsourcing services such as Amazon Mechanical Turk which provides noisy labels from non-experts at a f
In this paper we report the results of a pilot study comparing the older and younger adults interaction with an Android TV application which enables users to detect errors in video subtitles. Overall, the interaction with the TV-mediated crowdsourcin
Modern machine learning is migrating to the era of complex models, which requires a plethora of well-annotated data. While crowdsourcing is a promising tool to achieve this goal, existing crowdsourcing approaches barely acquire a sufficient amount of