From oximeters to AI, where bias in medical devices may lurk
Analysis: issues with some gadgets could contribute to poorer outcomes for women and people of colour
Deb Raji on Twitter
These are the four most popular misconceptions people have about race & gender bias in algorithms.I'm wary of wading into this conversation again, but it's important to acknowledge the research that refutes each point, despite it feeling counter-intuitive.Let me clarify.👇🏾 https://t.co/WdzmnGLaFm— Deb Raji (@rajiinio) March 27, 2021
Gender Shades
Computer vision technology has lower accuracy rates for black females
How Data Can Map and Make Racial Inequality More Visible (If Done Responsibly) | by The GovLab | Data Stewards Network | Medium
Racism is a systemic issue that pervades every aspect of life in the United States and around the world. In recent months, its corrosive…
Ongoing Data-Driven Efforts to Address Racial Inequality
Many of the issues discussed about data & racial inequality have been the focus by numerous organizations. This is a list of these organisations (largely US based)
Data racism: a new frontier - European Network Against Racism
Perhaps without even noticing, you have read about data racism several times in the news in the past months. What is it? This blog seeks to explain - in the context of an emerging strand of work at the European Network Against Racism exploring racism in the digital space.
Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms | NEJM
"By embedding race into the basic data and decisions of health care, these algorithms propagate race-based medicine. Many of these race-adjusted algorithms guide decisions in ways that may direct more attention or resources to white patients than to members of racial and ethnic minorities"
Data driven profiling: The hardwiring of discriminatory policing practices across Europe
Discusses harms of racial policing practices, potential effects of data driven technologies & opportunities for activism
An AI saw a cropped photo of AOC. It autocompleted her wearing a bikini.
Image-generation algorithms are regurgitating the same sexist, racist ideas that exist on the internet.
The dividing line: how we represent race in data – The ODI
The point of this essay is to encourage a critical approach to the relationship between race and data. It points to three questions that anyone working with data should ask if they are going to be collecting and using data about race.
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If we are not careful, data can divide and sort us in exactly the sort of essentialising ways that the colonial idea of race supported. But if researchers ask the right questions, and know their history, we can use data to advocate for racial justice.