Machine Learning and Education: Identifying and Mitigating Bias

VIRTUAL EVENT: THURSDAY, OCTOBER 21, 2021 | 4:30 – 5:30 pm ET

Machine Learning and Education: Identifying and Mitigating Bias
Many common machine learning algorithms can be heavily affected by biased data. This can lead to many inaccuracies and unwanted behavior in our machine learning models.

Tune in as our panelists give an overview of how seemingly objective applications can lead well-meaning practitioners astray. They will discuss the efforts underway to mitigate the risk associated with these scenarios, a range of tools available, and how these issues fit within the larger educational technology field.

Panelists:
Phil Horwitz, Chief Architect at JBS Custom Software Solutions
Dr. Andrew Hampton, Assistant Professor of Psychology at Christian Brothers University, and Chair of the IEEE Standards Association Working Group for Adaptive Instructional Systems
Joe Pringle, Principal Technical Business Development, AI and machine learning at Amazon Web Services

Save your seat, register here: https://bit.ly/3mv0u1

Busy at that time? Sign-up anyway and we'll send you the video recording. We look forward to seeing you online on October 21st. Bring your thoughts and questions!