An introduction to harm that ML systems cause and 10 concrete ways to build fairer ML systems

Photo of Black Lives Matter protesters in Washington, D.C. — 2 signs say “Black Lives Matter” and “White Silence is Violence”
Photo of Black Lives Matter protesters in Washington, D.C. — 2 signs say “Black Lives Matter” and “White Silence is Violence”
Photo by Koshu Kunii on Unsplash

Machine learning systems are increasingly used as tools of oppression. All too often, they’re used in high-stakes processes without participants’ consent and with no reasonable opportunity for participants to contest the system’s decisions — like when risk assessment systems are used by child welfare services to identify at-risk children; when a machine learning (or “ML”) model decides who sees which online ads for employment, housing, or credit opportunities; or when facial recognition systems are used to surveil neighborhoods where Black and Brown people live.

ML systems are deployed widely because they are viewed as “neutral” and “objective.” In reality though…


A two-part series on how we will measure discrepancies in Airbnb guest acceptance rates using anonymized perceived demographic data. This first part gives an overview of the privacy model of p-sensitive k-anonymity that we use.

Photo of a family of five (two parents, three kids) reading together under a blanket on a couch in an Airbnb
Photo of a family of five (two parents, three kids) reading together under a blanket on a couch in an Airbnb

Introduction

In June, the Airbnb Anti-Discrimination product team announced Project Lighthouse, an initiative with the goal to measure and combat discrimination when booking or hosting on Airbnb. We launched this project in partnership with Color Of Change, the nation’s largest online racial justice organization with over 7 million members, as well as with guidance from other leading civil rights and privacy rights organizations.

At the core…


How we started, what we’ve accomplished, and where we’re headed

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Spring 2017 Hack4Impact Retreat

In August 2014, I stepped onto the University of Pennsylvania’s campus as excited and optimistic as any of the freshmen, but also aware of the divide between members of the incoming class and me. This was my second time attending new student orientation, my second time navigating a new campus, my second time trying to fit into a community of thousands: I transferred to Penn at the beginning of my sophomore year.

I attended a Hack4Impact information session on a whim. A flyer was shoved into my hand during a club fair; as a new organization, Hack4Impact had no assigned…


How we started, what we’ve accomplished, and where we’re headed

Image for post
Image for post
Spring 2017 Hack4Impact Retreat

In August 2014, I stepped onto the University of Pennsylvania’s campus as excited and optimistic as any of the freshmen, but also aware of the divide between members of the incoming class and me. This was my second time attending new student orientation, my second time navigating a new campus, my second time trying to fit into a community of thousands: I transferred to Penn at the beginning of my sophomore year.

I attended a Hack4Impact information session on a whim. A flyer was shoved into my hand during a club fair; as a new organization, Hack4Impact had no assigned…

Skyler Wharton

Software Engineer (ML & Backend) @ Airbnb. I care about ethical AI/ML, mentorship/sponsorship, and diversity & inclusion. My opinions are my own. [they/them]

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