Impact of Data Regulations and Bias on Operational ML

Lead and moderator: Sandeep Uttamchandani, Intuit

Panelists: Kapil Surlaker, LinkedIn; Sean Grullon, GSK; Sendil Thangavelu, Mosaic—Solar FinTech Company; Arthur Roberts, HealthIQ

Abstract: 

Data is the oil that fuels ML models across Finance, Healthcare, Retail, and every vertical industry today. The data collected from customers either directly or indirectly (using behavioral analytics) is being increasingly regulated. EU’s General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), are among several upcoming regulations for data compliance. Additionally, checks-and-bounds are required to detect bias and improve trust in ML-based services.

This panel brings together top experts across the industry to discuss how they are dealing with regulations and bias, and its impact on real-world ML deployments. In particular, we unpack the key roadblocks in implementing regulatory compliance, tools/frameworks that are required, impact on ML model lifecycle. We wrap up with panelists sharing their emerging insights on detecting bias to avoid issues in ML-based services.

BibTeX
@conference {233011,
title = {Impact of Data Regulations and Bias on Operational {ML}},
year = {2019},
address = {Santa Clara, CA},
publisher = {{USENIX} Association},
}