Registration opens May 2019 San Diego,CA September 18-21, 2019

2019 Tapia Conference

Dealing with Bias and Unfairness in Machine Learning Algorithms

Friday, September 21, 2018 — 10:30AM - 12:00PM

Machine learning algorithms can encode a discriminative bias when training them with real data in which underrepresented groups are not properly characterized. Then a question quickly emerges: how can we make sure ML does not discriminate against people from minority groups because of the color of their skin, gender, or ethnicity? Even more, as the tech industry does not represent the entire population, underrepresented populations in computing such as Hispanics, women, African-Americans, Native Americans have limited control over the direction of machine learning breakthroughs. In this panel, we claim that it is our responsibility to advance the progress of machine learning by exposing this problem and proposing reliable solutions based on solid research. This will be done by increasing the presence of members of underrepresented groups that are able to build solutions and algorithms to advance the progress of this field towards a direction in which bias and unfairness are accordingly addressed.

Panel Moderator:
Patricia Ordóñez Franco, University of Puerto Rico Río Piedras
Omar Florez, Capital One Research
Juan E. Gilbert, University of Florida
Alonso Martinez, Pixar Animation Studios
Vicente Ordóñez Román
Laura Montoya, Accel.AI