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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

Abstract

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

fast.ai 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 fast.ai 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

Tracks

  • Groking Timeseries & Sequential Data

    Techniques, practices, and approaches, including image recognition, NLP, predictions, & modeling.

  • Deep Learning in Practice

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe including use cases on machine translation, computer vision, & image recogition.

  • AI Meets the Physical World

    Where AI touches the physical world, think drones, ROS, NVidia, TPU and more.

  • Papers to Production: CS in the Real World

    Groundbreaking papers make real world impact.

  • Solving Software Engineering Problems with Machine Learning

    Anomaly detection, ML in IDE's, bayesian optimization for config. Machine Learning techniques for more effective software engineering.

  • Predictive Architectures in the Real World

    Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix.