Suchergebnisse

Crowdsourcing tasks have been widely used to collect a large number of human labels at scale. While some of these tasks are deployed by requesters and performed only once by crowd workers, others require the same worker to perform the same task or a variant of it more than once, thus participating in a so-called
longitudinal study
. Despite the prevalence of longitudinal studies in crowdsourcing, there is a limited understanding of factors that influence worker participation in them across different crowdsourcing marketplaces. We present results from a large-scale survey of 300 workers on 3 different micro-task crowdsourcing platforms: Amazon Mechanical Turk, Prolific and Toloka. The aim is to understand how longitudinal studies are performed using crowdsourcing. We collect answers about 547 experiences and we analyze them both quantitatively and qualitatively. We synthesize 17 take-home messages about longitudinal studies together with 8 recommendations for task requesters and 5 best practices for crowdsourcing platforms to adequately conduct and support such kinds of studies. We release the survey and the data at: https://osf.io/h4du9/.

Web search is among the most frequent online activities. In this context, widespread informational queries entail user intentions to obtain knowledge with respect to a particular topic or domain. To serve learning needs better, recent research in the field of interactive information retrieval has advocated the importance of moving beyond relevance ranking of search results and considering a user's knowledge state within learning oriented search sessions. Prior work has investigated the use of supervised models to predict a user's knowledge gain and knowledge state from user interactions during a search session. However, the characteristics of the resources that a user interacts with have neither been sufficiently explored, nor exploited in this task. In this work, we introduce a novel set of resource-centric features and demonstrate their capacity to significantly improve supervised models for the task of predicting knowledge gain and knowledge state of users in Web search sessions. We make important contributions, given that reliable training data for such tasks is sparse and costly to obtain. We introduce various feature selection strategies geared towards selecting a limited subset of effective and generalizable features.

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues. © 2020 The Authors. WIREs Data Mining and Knowledge Discovery published by Wiley Periodicals, Inc.