Panels

Abstract – In machine learning, bias is defined pedagogically as the distance between the prediction of a value and its true value.  Colloquially, bias is an error induced by assumptions, unproven or otherwise.  Socially, bias is exhibited prejudice in favor of (or against) a person or a group of people.  These three definitions of the same term conflate uniquely in machine learning as decisions made by these models continue to proliferate our daily lives.  The decisions not only determine the allocation of network resources and the speed and orientation of autonomous vehicles, they also affect facial recognition, active policing, surveillance, border crossings, hiring, recidivism, health care, college admissions and much more.  A fair and bias-free model is an inescapable requirement especially as these models make decisions that impact lives and inform public policy.

This panel of experts will address the quality and reliability of the decisions made by systems that use machine learning models.  This panel will discuss the potential to identify bias when it appears in our systems, assist in that process by raising awareness of the reliability and quality of AI and ML models, examine the influence of sources of bias, and identify how to create fair models.

Speakers include

• Michael Tsikerdekis, PhD, Department of Computer Science, Western Washington University
• Pejman Khadivi, PhD, Department of Computer Science, Seattle University
• Prof. Cynthia Hood, Department of Computer Science, Illinois Institute of Technology
• Speaker 4

Speakers include

• Sridhar Rajagopal, Mavenir Systems
  Sridhar Rajagopal is a Vice President at Mavenir Systems, where he heads the system design for Mavenir’s virtualized cloud RAN products for LTE and 5G. Prior to this, he was VP, system engineering as one of the initial employees at Ranzure Networks, a cloud RAN start-up. He also had R&D roles in design, prototyping and standardization of 5G cellular and Wi-Fi systems at Samsung, in UWB technology at WiQuest communications and 3G/4G research at Nokia. He was an associate editor for the Journal of Signal Processing Systems (Springer) and has held leadership positions in standardization bodies such as IEEE and WiMedia. He was a co-recipient of the IEEE 2017 Marconi Prize Paper award for his research on mmWave systems. He has co-invented around 44 issued US patents. He received his M.S. and Ph.D. degrees from Rice University and is a senior member of IEEE.
• Geoff Hollingworth, MobiledgeX
  Geoff is overall responsible for marketing at MobiledgeX Inc. MobiledgeX’s mission is to delight developers with global edge services which are easy, valuable, trusted and mobile first. Previously at Ericsson, Geoff drove the global positioning, promotion and education of Ericsson’s approach to next generation infrastructure to support future 5G and IOT growth with the right economic. Geoff was embedded with AT&T in Silicon Valley, leading Ericsson’s innovation efforts towards the AT&T Foundry initiative. He has also held positions as Head of IP Services Strategy for North America and overseeing the Ericsson brand in North America, as well as other roles in software R&D and mobile network deployment. Joining Ericsson more than 20 years ago, Geoff has been based in London, Stockholm, Dallas and Silicon Valley. The only other place Geoff has worked is CERN.He holds a First Class Honors Bachelors degree in Computing Science and has won the Computing Science Prize of Excellence from Aston University in Birmingham, United Kingdom.
  “I am a marketing person first, software guy second and love creating passionate successful teams.”
• Speaker 3