"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"

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. § 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.

Centre for Critical Race and Digital Studies. List of publications, books, reports and videos regarding the intersection of race & digital/data/tech.

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

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)

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.

Racism is a systemic issue that pervades every aspect of life in the United States and around the world. In recent months, its corrosive…

“It’s almost an imperative, I think, to drive that diversity,” she said. “Diversity from a gender perspective, but also from other perspectives such as age, race, ethnicity, geography, and many others, because we’re seeing AI is such a powerful technology, and we need to make sure it is equitable.”

Discusses harms of racial policing practices, potential effects of data driven technologies & opportunities for activism

Computer vision technology has lower accuracy rates for black females

Image-generation algorithms are regurgitating the same sexist, racist ideas that exist on the internet.

Analysis: issues with some gadgets could contribute to poorer outcomes for women and people of colour

New research and Facebook’s own ad archive show that the company’s new system to ensure diverse audiences for housing and employment ads has many of the same problems as its predecessor.

Artificially intelligent analysis of job applications or videos is "pseudoscience", researchers say.

As we hand over decision-making regarding social issues to automated systems developed by profit-driven corporates, not only are we allowing our social concerns to be dictated by the profit incentive, but we are also handing over moral and ethical questions to the corporate world, argues ABEBA BIRHANE

Education technology powered by artificial intelligence appears to make classrooms more equitable, but the tech may do more harm than good.

Here are 18 books written by scholars and technology experts that cut through all the hype and will forever change the way you think about…

Proctored exams have a bias toward students from racial minorities and other disadvantaged groups.

Ed Bridges, 37, from Cardiff, brought a legal challenge after having his image captured twice.

Study reveals rampant racism in decision-making software used by US hospitals — and highlights ways to correct it.

Tools like GPT-3 are stunningly good, but they feed on the cesspits of the internet. How can we make them safe for the public to actually use? § Sometimes, to reckon with the effects of biased training data is to realize that the app shouldn't be built. That without human supervision, there is no way to stop the app from saying problematic stuff to its users, and that it's unacceptable to let it do so.

A blog examining the legal implications of the use of AI in the workplace.

The use of artificial intelligence (AI) presents risks to equality, potentially embedding bias and discrimination. Auditing tools are often promised as a solution. However our new research, which examines tools for auditing AI used in recruitment, finds these tools are often inadequate in ensuring compliance with UK Equality Law, good governance and best practice. We argue in this report that a more comprehensive approach than technical auditing is needed to safeguard equality in the use of AI for hiring, which shapes access to work. Here, we present first steps which could be taken to achieve this. We also publish a prototype AI Equality Impact Assessment which we plan to develop and pilot.

This year, in honor of Black History Month, the Codecademy Team is celebrating Black leaders that are working to build a more inclusive, more welcoming, and more diverse tech industry. It's important to celebrate Black people in all our roles and diversity. For UK Black History Month (BHM), we're keen to see similar profiling of technologists who want to raise their visibility, so we can celebrate their work.