Lessons Learned: An ethics roadmap for real-time predictions

October 15, 2018
4:00pm — 5:00pm ET

AI Ethics in practice lecture with Soyean Kim, Tech Safety BC

Moderated by Gretchen Greene. Jointly hosted with Harvard Berkman Klein Center Working Theories Working Group.

Ethics is increasingly becoming a buzz word in the world of machine learning. While small and large companies are continuing to innovate their operations with data-driven, predictive algorithms, it remains a challenge for companies to identify what ethical issues are relevant to them and how much risk the issues pose to their business. 

In this talk, Kim shares the findings and lessons learned from generating an ethics roadmap at the onset of integrating real-time predictions into the operations of Technical Safety BC, a safety regulator in Canada.

Soyean Kim is a professional statistician (P.STAT) who is the chair of Accreditation Committee at Statistical Society of Canada. She currently leads a team of data scientists at Technical Safety BC, a safety regulator in Canada. Her key contribution includes advancing ethics roadmap in predictive system and deployment of AI and machine learning to help safety inspection process. Her previous leadership roles include her tenure at PricewaterhouseCoopers and Fortis as a rate design manager. She is an advocate for “Data for Good” and a speaker on the topic of real world applications of AI. Her latest speaking engagement includes PAPIs in London, UK which is a series of international AI conferences, and BC Tech Summit in Vancouver.

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