On the sources of datasets AI uses for training.

Executive concerns of “over-enforcement” let hate speech through, report says.

Researchers from Ireland, the UK and the US have warned that the growth in hyperscale AI training datasets threaten to propagate the worst aspects of their internet sources, contending that a recently-released academic dataset features ‘troublesome and explicit images and text pairs of rape, pornography, malign stereotypes, racist and ethnic slurs, and other extremely problematic […]

Timnit Gebru, Margaret Mitchell, and other leaders discuss tensions surrounding AI ethics in big tech and what teams need to succeed.

The reanimation of the pseudosciences of physiognomy and phrenology at scale through computer vision and machine learning is a matter of urgent concern. This Ar

The development and deployment of artificial intelligence (AI) in all areas of public life have raised many concerns about the harmful consequences on society, in particular the impact on marginalised communities. EDRi's latest report "Beyond Debiasing: Regulating AI and its Inequalities", authored by Agathe Balayn and Dr. Seda Gürses,* argues that policymakers must tackle the root causes of the power imbalances caused by the pervasive use of AI systems. In promoting technical ‘debiasing’ as the main solution to AI driven structural inequality, we risk vastly underestimating the scale of the social, economic and political problems AI systems can inflict.

Policymakers around the world are increasingly considering how to prevent government uses of algorithms from producing injustices. One mechanism that has become

n.b. leads directly to fewer scholarships

Automated software relies on overly-simplistic criteria

Government agencies are attempting to measure people’s trust in AI without considering whether the AI is trustworthy

In early 2018, officials at University College London were shocked to learn that meetings organized by “race scientists” and neo-Nazis,…

A new study reveals models aren’t reporting enough, leaving users blind to potential model errors such as flawed training data and calibration drift.

In a sea of data, the use of student success analytics may unintentionally result in consequences that benefit some students while harming others.

Experts say medical images like X-Rays and CT scans allow algorithms to determine a patient's race—and warn it could lead to bias and discrimination.

AI researcher and former Googler Margaret Mitchell spoke about ethics challenges at VentureBeat's Transform 2021 event today.

Misogyny on the internet too often slips through the filters of content moderators. A new method hopes to inject more nuance into the process.

Some have been used in hospitals, despite not being properly tested. But the pandemic could help make medical AI better.

THIS SHIT DOESN'T EVEN WORK.