“Biased by Design: Why Data Ethics Must Lead the AI Revolution”
327 | Fri 01 Aug 2 p.m.–2:45 p.m.
Presented by
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Autumn is a product manager at Microsoft Azure specializing in Linux security. In her previous role at AWS as a software engineer, she focused on the development and release of Amazon Corretto (Java) while actively engaging in the OpenJDK community; before that, she worked as an AWS NoSQL Solutions Architect and created educational content in Python and Java.
Autumn co-hosts the exciting new "Fork Around and Find Out" podcast, sharing stories on tech lessons learned, with her previous co-host of the popular "Ship It!" podcast. A proud mom and "Rewriting the Code" alumni, Autumn serves as the Board Chair of Education at MilSpouse Coders, leading the chapter in the Greater Seattle Area, due to her advocacy for collaborative learning and community development.
https://www.linkedin.com/in/autumn-nash/
Abstract
As AI technologies weave themselves into every aspect of our lives — from healthcare to hiring, policing to personal assistants — the biases hidden in the data we feed them are shaping real-world outcomes in ways we often don’t see until it’s too late. This talk will explore why bias in data isn’t just a technical bug but a reflection of historical, social, and systemic inequities. We’ll dive into real-world examples where unchecked data bias led to harmful consequences, unpack how even well-intentioned AI projects can reinforce discrimination, and outline strategies for building more ethical, inclusive systems from the ground up. Whether you’re an engineer, product manager, or leader, understanding the role of bias isn’t optional — it’s essential for building AI we can trust.
As AI technologies weave themselves into every aspect of our lives — from healthcare to hiring, policing to personal assistants — the biases hidden in the data we feed them are shaping real-world outcomes in ways we often don’t see until it’s too late. This talk will explore why bias in data isn’t just a technical bug but a reflection of historical, social, and systemic inequities. We’ll dive into real-world examples where unchecked data bias led to harmful consequences, unpack how even well-intentioned AI projects can reinforce discrimination, and outline strategies for building more ethical, inclusive systems from the ground up. Whether you’re an engineer, product manager, or leader, understanding the role of bias isn’t optional — it’s essential for building AI we can trust.