Dealing with Bias and Unfairness in Machine Learning Algorithms
Friday, September 21, 2018 — 1:30PM - 3:00PM
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: Omar Florez, Capital One Research