Digital Mental Health Resources and Digital Workers Laura Nguyen, Emily Gringorten, Bryson McClure, Sanjana Dutt, and Saiph Savage.Sandy Gould, Laura Lascau, Duncan Brumby, and Anna L.Reading Comprehension across Physical and Digital Mediums ![]() Hadeel Farhan, Daniel Krasnonosenkikh, Gwendolyn Payne, Luca Sharbani, and Saiph Savage.Toward User-adaptive Interactive Labeling on Crowdsourcing Platforms Merlin Knaeble, Mario Nadj, Alexander Maedche, and Nico Loewe.Doing Work from Where You Are: AI HIT Management Dispatching Work Tailored to Context In this talk, I deep dive in two main topics, personalization and search at, how bias affects the machine learning algorithms and the strategies we use to mitigate it. However, implicit feedback carries the biases that are introduced by our own algorithms and the limitations of how information is presented. Many machine learning solutions rely on the learnings from implicit human feedback. This creates a vicious cycle, introducing bias that is constantly reinforced by human implicit feedback. At the same time that we try to predict users' intent and facilitate access to certain information, we implicitly learn from users' actions what are their preferences to support these predictions. In these examples, content, shape, position and order in which information is presented, have a substantial impact on human decision and a significant impact in business KPIs. Personalization, recommendations and search are some examples where we already apply state of the art machine learning algorithms. Since a few years, is heavily investing in machine learning solutions to create a better online experience for its users. ![]() Finally, as a specific interest area, the workshop seeks to study crowdwork from the context of the Global South, which has been arising as an important but critically understudied crowdsourcing market in recent years. We invite visionary takes in various formats on this topic to spread awareness of worker-centric research and developments to the CHI community.Īs a result of interactive ideation work in the workshop, we articulate a future direction roadmap for research centred around crowdsourcing platforms. from better processes for worker onboarding, skills-development, and growth. This includes not only the workers but also the platforms, who could benefit e.g. This workshop explores how a reimagined perspective on crowdsourcing platforms could provide a more equitable, fair, and rewarding experience. The HCI community has been increasingly interested in investigating the worker-centric issues inherent in the current model and seeking for potential improvements that could be implemented in the future. Crowdworkers silently enable much of today's AI-based products, with several online platforms offering a myriad of data labelling and content moderation tasks through convenient labour marketplaces.
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