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Crowd Computing & Human-Centered AI
The Academic Fringe Festival is an exciting concoction of invited talks and panel discussions around important themes of research and innovation in Computer Science. This first edition is on "Crowd Computing and Human-Centered AI". The series features prominent researchers and practitioners, whose work has made fundamental contributions in these fields.
The Academic Fringe Festival is an exciting concoction of invited talks and panel discussions around important themes of research and innovation in Computer Science. This first edition is on "Crowd Computing and Human-Centered AI". The series features prominent researchers and practitioners, whose work has made fundamental contributions in these fields.
The unprecedented rise in the adoption of artificial intelligence techniques in many contexts is concomitant with shortcomings of such technology with respect to robustness, interpretability, usability, and trustworthiness. Crowd computing offers a viable means to engage a large number of human participants in data related tasks and in user studies. In the context of overcoming the computational and interactional challenges facing the current generation of AI systems, recent work has shown how crowd computing can be leveraged to either debug noisy training data in machine learning systems, understand which machine learning models are more congruent to human understanding in particular tasks, or to advance our understanding of how AI systems can influence human behavior.
The unprecedented rise in the adoption of artificial intelligence techniques in many contexts is concomitant with shortcomings of such technology with respect to robustness, interpretability, usability, and trustworthiness. Crowd computing offers a viable means to engage a large number of human participants in data related tasks and in user studies. In the context of overcoming the computational and interactional challenges facing the current generation of AI systems, recent work has shown how crowd computing can be leveraged to either debug noisy training data in machine learning systems, understand which machine learning models are more congruent to human understanding in particular tasks, or to advance our understanding of how AI systems can influence human behavior.
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The Speakers
The Speakers
Matthew Lease
Matthew Lease
Adventures in Crowdsourcing: Toward Safer Content Moderation and Better Supporting Complex Annotation Tasks [more details]
Adventures in Crowdsourcing: Toward Safer Content Moderation and Better Supporting Complex Annotation Tasks [more details]
University of Texas at Austin, Amazon Scholar
Gianluca Demartini
Gianluca Demartini
University of Queensland
Shamsi Iqbal
Shamsi Iqbal
Microsoft
Panos Ipeirotis
Panos Ipeirotis
New York University
Michael Bernstein
Michael Bernstein
Stanford University
Olga Megorskaya
Olga Megorskaya
Toloka
Simo Hosio
Simo Hosio
University of Oulu
Edith Law
Edith Law
Crowdsourcing Medical Time Series Annotation: Expertise, Ambiguity and Human-AI Collaboration [more details]
Crowdsourcing Medical Time Series Annotation: Expertise, Ambiguity and Human-AI Collaboration [more details]
University of Waterloo
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