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

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

The UK government admits it knew its facial mapping tech struggled to work with some skin tones.

Joy Buolamwini and Timnit Gebru investigated the bias of AI facial recognition programs. The study reveals that popular applications that are already part of the programming display obvious discrimination on the basis of gender or skin color. One reason for the unfair results can be found in erroneous or incomplete data sets on which the program is being trained. In things like medical applications, this can be a problem: simple convolutional neural nets are already as capable of detecting melanoma (malignant skin changes) as experts are. However, skin color information is crucial to this p...

This project is part of the Ars Electronica CyberArts Exhibition. Algorithms have become essential elements of our daily lives, used in almost all areas of society: in online searches and navigation, in ratings systems and smart devices or bots, but also in banking, speech and facial recognition, health care, policing, and so on. However, the systems that are developed are never neutral, which means that algorithms may be biased and discriminatory. The Algorithmic Justice League (AJL) is an organization that combines art and research to increase public awareness of the social implications a...

AI Ethics: Global Perspectives is a free, online course designed to raise awareness and help institutions work toward a more responsible use of AI. It brings together leading experts in the field of AI from around the world to consider ethical ramifications of AI and rectify initiatives that might be harmful to particular people and groups in society. We will release videos on a rolling basis. Sign up at bit.ly/ai-ethics-course to get notified about the launch of the course; and future releases!

Machines can discriminate in harmful ways. Here's how we fix the problem

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

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

Machine learning has the potential to save thousands of people from skin cancer each year—while putting others at greater risk.

Inaction makes you part of the problem. § In the UK, as protesters fill Hyde Park today, many are aware that while systemic racism may not result in as many horrific deaths of black people at the hands of police as in the US, racism is still pervasive within our society and politics.

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.

Many of the public and private services we use are now digital. The move to digital is likely to increase as technology becomes more embedded in our lives. But what does this mean for how essential public services understand who is using, or indeed not using, them and why? § Data about the protected characteristics of people using these services isn’t currently collected andstatistics aren’t published in a consistent or collective way. This means it is harder tofind out who is excluded from using these services and why.

Branches of Co-op in the south of England have been using real-time facial recognition cameras to scan shoppers entering stores. § See US based studies on FRT which shows the technology can be unreliable for black people, especially black women.

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.