David Corliss examines how shifting the focus to testing for and mitigating bias at the design stage instead of after code is released can help prevent many of the problems seen in machine learning and artificial intelligence.
Algorithm
Equity and Bias in Algorithms: A Discussion of the Landscape and Techniques for Practitioners
Algorithmic bias can occur as a result of decisions made throughout the algorithm development and deployment process. Left unaddressed, it can deeply affect equity. Emily Hadley looks at techniques to consider when developing algorithms.
Data Science Competition Attracts Talent from UGA
The first data science competition hosted by the department of statistics at the University of Georgia attracted 96 students, with 10 undergraduate teams and 16 graduate teams. Most teams had students from multiple disciplines, making the competition truly interdisciplinary.