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Keynote: Analyzing & Preventing Unconscious Bias in Machine Learning

Location: Cyril Magnin Ballroom

Duration: 4:00pm - 5:00pm

Day of week: Wednesday


Increasingly AI is finding its way into nearly every product we use (everything from photo sharing apps to criminal justice decision algorithms), but often various types of bias are buried in the underlying data and models.  This can have a damaging impact on both individuals and society. Through the lens of 3 case studies, we will look at how to diagnose bias, identify some sources, and take steps to avoid it.

Speaker: Rachel Thomas founder & USF assistant professor

Rachel Thomas has a math PhD from Duke and was selected by Forbes as one of “20 Incredible Women Advancing AI Research.” She is co-founder of and a researcher-in-residence at the University of San Francisco Data Institute, where she teaches in the Masters in Data Science program. Her background includes working as a quant in energy trading, a data scientist + backend engineer at Uber, and a full-stack software instructor at Hackbright.

Find Rachel Thomas at

2019 Tracks

  • Groking Timeseries & Sequential Data

    Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.