The BC Centre for Disease Control (BCCDC) is organizing a special seminar series of Machine Learning for Precision Public Health as part of our large effort to incorporate more data science into our work. These presentations and workshops will cover topics of data-driven decision making, machine learning algorithms, data visualization, and big data ethics. Our next sessions are coming up on Wed April 10, 2019:
BCCDC’s Grand Rounds presentation from 12:00 – 1:00 PM, Who’s Afraid of the Big (Bad) Data? Ethical and Political Considerations, by Dr. Diego Silva
- Data sciences (including 'big data', AI, and machine learning) forces us to reconsider a host of traditional bioethical and political challenge related to privacy and confidentiality, data sharing, and data security. However, the field of data sciences also brings up newer and 'deeper' philosophical questions about notions of personhood, self-knowledge, and autonomy, which in turn shape our understanding of traditional bioethical issues and principles. In this presentation, I will argue that a greater appreciation of these deeper issues are imperative to consider as the importance and prominence of data sciences grows within medicine and public health. No live webcasting is available due to ongoing technical issues. Recording will be available on this link.
Discussions Session from 1:15 – 3PM, Ethical Considerations In The Era Of Big Data, by Drs. Diego Silva and Mark Gilbert
- This session provides an opportunity for participants to discuss ethical challenges encountered or anticipated in applying data science approaches in public health work. Using a small-group discussions format, we will review an existing ethics framework, discuss scenarios where data science is applied in public health, and identify ethical considerations needed for decision making in this new era of big data. Registration is required for this session as spaces are limited.
Both sessions will be held in Hardwick Hall of the UBC Medical Student & Alumni Centre (2750 Heather St, Vancouver.) For questions or more information, contact Hsiu-Ju Chang (firstname.lastname@example.org) or Mike Irvine (email@example.com).