
ATARC 2022 AI & Data Virtual Summit
June 28, 2022, 10:00 AM - 12:15 PM ET2.5 CPE Credits Available for this Event***
10:00 AM
Visionary Keynote

Ted Kaouk
Chief Data Officer, Office of the Chief Information Officer [OCIO], United States Office of Personnel Management (invited)
10:15 AM
Emerging Technology Tech Talks
10:30 AM
Panel: Can Artificial Intelligence Help Increase Fairness and Equity?
AI systems need to be ethical, responsible, transparent, and explainable. AI and ML systems are greatly dependent upon their training data to ensure results from the algorithms are fair and ethical. These concerns are particularly important for government decision making that may affect citizens and services.
Listen in as topic experts explore how the use of artificial intelligence to increase fairness and equity has become important throughout governmental decision-making. How should the government validate, measure, and ensure the ethics and fairness of AI systems and decisions?

Chakib Chraibi
Chief Data Scientist and ODS Acting Associate Director, National Technical Information Service, U.S. Department of Commerce

Chris Rottler
Associate Chief Data Officer, Food and Nutrition Service, U.S. Department of Agriculture (invited)

Dr. Lynne Parker
Assistant Director for Artificial Intelligence, Technology Division, Office of Science and Technology Policy, Executive Office of the President (invited)

Donna Murphy
Deputy Comptroller for Compliance Risk, Office of Compliance and Community Affairs, U.S. Department of the Treasury (invited)

Moderator: Melissa Harris
Senior Researcher, GovCIO
11:15 AM
Emerging Technology Tech Talks
11:30 AM
Panel: Let AI Bring BIG Value out of Big Data
As government mission requirements grow, Federal agencies continuously seek ways to maximize the use of the vast data sets they collect and store. Artificial Intelligence (AI) is a collection of technologies that excel at extracting insights and patterns from large sets of data. By using those insights and patterns to make future predictions, AI has the potential to improve mission effectiveness, stretch workforce capacity, combat waste, fraud, and abuse and drive operational efficiencies.
Tune into this panel as Federal CDO’s discuss their ever-changing data-centric environment combining real-time data streams, established databases, and legacy datasets. What are some major successes and challenges that occur at this intersection of finding value in the collected information. How can agencies best harness the AI potential to transform national security, agriculture, transportation and healthcare? What steps are completed and planned to leverage AI towards improvement in overall government operations?

Scott Beliveau
Chief Data Officer (Acting), U.S. Patent and Trademark Office

Brien Lorenze
Chief Data Officer, Pandemic Response Accountability Committee, Executive Office of the President

Oliver Wise
Chief Data Officer, Office of the Under Secretary for Economic Affairs, Department of Commerce

Dr. Lon Gowen
Chief Data Officer, Office of the Chief Information Officer, Office of the Under Secretary, United States Department of Homeland Security (pending agency approval)

Thomas Sasala
Chief Data Officer, Office of the Chief Information Officer, Under Secretary of the Navy, United States Department of the Navy

Moderator: Jory Heckman
Reporter, Federal News Network

Zoom for Government enables ATARC remote collaboration opportunities through its cloud platform for video and audio conferencing, chats and webinars across all devices. Allowing for individuals from all areas of government, industry and academia to communicate directly.Â

*** ATARC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org