Browsing by Subject "human-centered data science"
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- Conference PaperApplying Human-Centered Data Science to Healthcare: Hyperlocal Modeling of COVID-19 Hospitalizations(Companion Proceedings of the 2023 ACM International Conference on Supporting Group Work, 2023) Chui, Victoria; Pater, Jessica; Toscos, Tammy; Guha, ShionAlgorithms as a component of decision-making in healthcare are becoming increasingly prevalent and AI in healthcare has become a topic of mass consideration. However, pursuing these methods without a human-centered framework can lead to bias, thus incorporating discrimination on behalf of the algorithm upon implementation. By examining each step of the design process from a human-centered perspective and incorporating stakeholder motivations, algorithmic implementation can become vastly useful, and more accurately tailored to stakeholder needs. We examine previous work in healthcare executed with a human-centered design, to analyze the multiple frameworks which effectively create human-centered application, as extended to healthcare.
- Conference PaperMapping Out Human-Centered Data Science: Methods, Approaches, and Best Practices(Companion Proceedings of the 2020 ACM International Conference on Supporting Group Work, 2020) Kogan, Marina; Halfaker, Aaron; Guha, Shion; Aragon, Cecilia; Muller, Michael; Geiger, StuartSocial media platforms and social network sites generate a multitude of digital trace behavioral data, the scale of which often necessitates the use of computational data science methods. On the other hand, the socio-behavioral and often relational nature of the social media data requires the attention to context of user activity traditionally associated with qualitative analysis. Human-Centered Data Science (HCDS) attempts to bridge this gap by both harnessing the power of computational techniques and accounting for highly situated and nuanced nature of the social media activity. In this workshop we plan to consider the methods, pitfalls, and approaches of how to do HCDS effectively. Moreover, from collating and organizing these approaches we hope to progress to considering best (or at least common) practices in HCDS.