Advancing Crowdsourced Wisdom: Rethinking What Information Is Gathered and How It Is Aggregated
In recent years, crowdsourcing has grown in prevalence and importance, evolving from a recreational activity into an invaluable societal resource. As a prominent example, this distributed form of work has amplified the usage of human abilities towards tasks that remain challenging for machine-only approaches. Yet its benefits remain largely untapped as the vast majority of crowdsourcing implementations elicit single numerical or binary responses, which are then aggregated via techniques that can be overly influenced by inadequacies inherent in these inputs. In this talk, Dr. Escobedo will describe his research group’s ongoing efforts to enhance the quality of collective intelligence that can be extracted within a variety of crowdsourcing tasks through the augmentation of input elicitation and aggregation. He will also discuss how these mechanisms can further benefit from the integration of machine learning techniques.
Adolfo R. Escobedo is an assistant professor in the School of Computing and Augmented Intelligence at ASU. He received a Ph.D. in Industrial & Systems Engineering from Texas A&M University in 2016 and a B.A. in Mathematics from California State University, Los Angeles in 2009. His active research areas include crowdsourcing, computational social choice, sustainable infrastructure development, and computational linear algebra. In conjunction with Erick Moreno-Centeno, Adolfo received an Honorable Mention in the 2015 INFORMS Junior Faculty Interest Group paper competition as a student at Texas A&M based on his work on roundoff error-free algorithms for optimization. He is also co-winner of the 2021 INFORMS Computing Society Prize.
1:30 – 2:15 pm AZ Talk
2:15 – 2:45 pm AZ Guided Q&A
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