12:30 – 1:00pm: Understanding and Addressing Bias in AI Diagnostic Systems
Speaker: Julie Vaughn, MIT
Abstract: Experts predict that artificial intelligence (AI) will completely revolutionize the field of medical diagnostics. However, we must consider the impact of bias. How do we recognize and address different kinds of bias, and avoid models that perpetuate bias in healthcare and other fields?
1:00 – 1:30pm: Data Science in Cloud Data Protection
Speaker: Shmily Wang, SAP
Abstract: In this talk, it will be talking about how to implement data science techniques to protect data on public cloud. In the meanwhile, it will also discuss how to deal with risks come along with that, given the needs to comply with various data-protection regulations like GDPR.
1:30 – 2:00pm: Laying the Foundation for Successful Model Deployment
Speaker: Hiranmayi Duvvuri, Vacasa
Abstract: You’ve started a new project, but have you thought through everything that could go wrong? Any new project needs an issue mitigation plan. The initial work sets up the foundation that ensures all parties are on board; with their responsibilities, uses of the model, and long/short-term plans.
2:00 – 2:30pm: Adverse Impact - a data science perspective on a legal data-evaluation process
Speaker: Elinor Brondwine, Workday
Abstract: Legal requirements are becoming a critical part of data science productization. We will examine current legal requirements for evaluating unintentional discriminatory effects via adverse impact, to build a better understanding of what the future may hold for algorithm evaluation.