Date: June 1 - 2, 2022


Event overview

e-Health Conference and Tradeshow brings Canadian digital health professionals together to network, connect, and exchange insights on transforming healthcare with technology.

Connect with Infosys Public Services

Infosys Public Services is hosting a session on Practicing Responsible AI. The session will cover the opportunities and risks associated with enabling responsible AI and an approach to build transparent and explainable AI models.



Dr. Suman De, Head of Government Healthcare Analytics, Infosys Public Services

Dr. Ritu Agarwal, Distinguished University Professor, University of Maryland

Diya Wynn, Senior Practice Manager, Emerging Technologies & Intelligent Platforms, AWS

Session details


Practicing Responsible AI: Risks, Challenges & Opportunities


June 1, 2022


12 PM – 12:30 PM


Ritu Agarwal, PhD, University of Maryland

Diya Wynn, Amazon Web Services


Dr. Suman De, Head of Government Healthcare Analytics, Infosys Public Services

Session Description

Democratization of technology and the pandemic has fueled AI/ML adoption across the public sector. Several public health agencies have leveraged AI/ML technologies to support their initiatives including the identification of vulnerable populations, delivery of personalized interventions, and elimination of fraudulent transactions.

While these AI-enabled initiatives generated new insights and enabled the agencies to improve outcomes, they also raised serious concerns regarding the bias in AI/ML adoption and renewed the focus on trust and transparency.

From changing regulations to increasing constituent trust and cost of building AI/ML models, data scientists, agency leaders and policy makers need to understand the challenges, opportunities and risks associated with AI. They need to adopt the right strategies and tactics to ensure AI systems are built responsibly, are explainable, use resources in an optimal manner, and engender trust.

Join this session for perspectives from experts on the definition and implementation of responsible AI and outline a governance framework built on people, process and technology pillars, to help organizations develop purposeful AI models

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