Session

Panel: Tackling Bias in AI

Artificial Intelligence has seen immense advancements in the 21st century, and its impact has been described as “the next electricity”. Yet, even the most superlative AI algorithms are only as good as the underlying training data – and this leads to bias in the algorithmic decisions. In this panel, our experts will discuss how deep and challenging are the actual biases in AI algorithms. Can we trust the algorithms deployed today (in medicine, finance, security, legal, HR) to accurately model and protect the interests of everyone. And if not, what techniques can be used to quantify and then eliminate those biases.

Biography

Celeste Fralick is the Senior Principal Engineer and Chief Data Scientist for McAfee. She is responsible for McAfee’s technical analytic strategy that integrates into McAfee consumer and enterprise products as well as internal Business Intelligence.

Celeste brings over 36 years of industry experience to McAfee. Prior to Intel’s divestiture of McAfee, she was Chief Data Scientist in Intel’s Internet of Things Group where she developed Machine Learning and Deep Learning analytics for over eight different markets. Celeste chairs McAfee’s Analytic Center of Excellence, and was a technical leader in Intel’s data science and biotech strategies, product qualification, and product life cycle programs.

Active in various industry and academic boards, journal editorial staffs, and consortiums, her experience spans analytics, systems, IoT engineering, semiconductor process and product development, regulatory, medical device, and quality/reliability domains. She holds a PhD in Biomedical Engineering from Arizona State University, concentrating on predictive analytics and neuroscience, and is currently authoring Springer’s ‘Intelligent Analytics’.

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