Over the past year, discourse about the ethical risks of machine learning has largely shifted from speculative fear about rogue super intelligent systems to critical examination of machine learning’s propensity to exacerbate patterns of discrimination in society. This talk explains how and why bias creeps into supervised machine learning systems and proposes a framework businesses can apply to hold algorithmic systems accountable in a way that is meaningful to people impacted by systems. You’ll learn why it’s important to consider bias throughout the entire machine learning product lifecycle (not just algorithms), how to assess tradeoffs between accuracy and explainability, and what technical solutions are available to reduce bias and promote fairness.
About the Speaker
Kathryn Hume is Vice President Product & Strategy for integrate.ai, a SaaS startup applying AI to drive growth and customer satisfaction for large enterprises, and a Venture Partner at ffVC, a seed- and early-stage technology venture capital firm, where she advises early-stage artificial intelligence companies and sources deal flow.
As the former Director of Sales and Marketing at Fast Forward Labs (Cloudera), Kathryn helped Fortune 500 companies accelerate their machine learning and data science capabilities. Prior to that, she was a Principal Consultant in Intapp’s Risk Practice, focused on data privacy, security, and compliance. A widely respected speaker and writer on AI, Kathryn excels at communicating how AI and machine learning technologies work in plain language. She has given lectures and taught courses on the intersections of technology, ethics, law, and society at Harvard Business School, Stanford, the MIT Media Lab, and the University of Calgary Faculty of Law. She speaks seven languages, and holds a PhD in comparative literature from Stanford University and a BA in mathematics from the University of Chicago.
Kathryn Hume is passionate about building companies and products that unlock the commercial value of emerging technologies. Alongside her work at integrate.ai, she is a Venture Partner at ffVC, a seed- and early-stage technology venture capital firm, where she advises early-stage artificial intelligence companies and sources deal flow. As the former Director of Sales and Marketing at Fast Forward Labs (Cloudera), Kathryn helped Fortune 500 companies accelerate their machine learning and data science capabilities. Prior to that, she was a Principal Consultant in Intapp’s Risk Practice, focused on data privacy, security, and compliance. A widely respected speaker and writer on AI, Kathryn excels at communicating how AI and machine learning technologies work in plain language. She has given lectures and taught courses on the intersections of technology, ethics, law, and society at Harvard Business School, Stanford, the MIT Media Lab, and the University of Calgary Faculty of Law. She speaks seven languages, and holds a PhD in comparative literature from Stanford University and a BA in mathematics from the University of Chicago.
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