The reasoning for this strange design was that the bridges would preserve the ability of white, wealthy Americans who could afford private cars to travel to Jones Beach, but not those who rely on tall buses for transportation.
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On the route to Long Island’s Jones Beach, 1920’s urban planner Robert Moses constructed a series of extraordinarily low bridges to filter people on and off the highway.
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Hannah Fry, a senior lecturer at University College London and author of Hello World: How to Be Human in the Age of the Machine, gives the example of racist bridges to explain how everyday infrastructure can be biased. And just as bias can be baked into physical infrastructure by either deliberate maliciousness or thoughtless omission, it enters algorithmic infrastructure in these same ways. These days, algorithms are as ubiquitous as infrastructure. While I took Tufecki’s advice and refused to build a model that would perpetuate systemic racism, navigating speaking up to a large university department and trying to prove the assignment would result in biased models made me reflect on two questions: What is algorithmic fairness, and what can we do to fix it? What is algorithmic fairness? Why is algorithmic bias important? A class in my master’s program assigned a problem set that utilized the popular and racist Boston Housing Dataset. It became more complicated, however, when I was later faced with the exact scenario Tufecki alluded to in her talk. When helping teach and develop the curriculum for Inspirit AI’s Introduction to AI camps, we ensured ethics and AI for social good became a central tenet of every lesson. I vowed then and there to adhere to an algorithmic Hippocratic Oath to do no harm and advocate for fair and ethical technology.Īt first, putting this oath to practice felt straightforward.
Sitting in the fifth row, hearing Tufecki’s call-to-action felt like lightning striking a metal rod somewhere inside me. I had skipped out on a week of school during my senior year at MIT to attend this conference. Tufecki was directly asking tech employees to become scientists for responsibility, using their collective voice and freedom of speech to refuse to build unethical algorithms and instead build alternatives. “I probably won’t be invited back for saying this… but organize, insist on having a voice, and refuse to build unethical technology.” In 2019, self-proclaimed “techno-sociologist” Zeynep Tufekci stood on stage at one of the largest machine learning conferences, sponsored by the likes of Facebook, Google, and Amazon, and in front of hundreds of tech employees stated:
In this third GDI Deep Read, Research Analyst Anna Sappington reflects on her own experiences with machine learning to explore the nuanced concept of ‘algorithmic fairness’ as a way to prevent both socially and technically embedded bias within real-world tools. Most media coverage discussing algorithmic biases has focused on social and cultural factors - yet technical biases also play a critical, yet largely invisible role. Welcome to 2021! Over the holidays, our team at GDI has been reflecting on the increasing impact of algorithms on everyday decision processes, especially given current world events.
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