Timnit Gebru

From language to vision models, deep neural networks are marked by improved performance, higher efficiency, and better generalizations. Yet, these systems are also marked by perpetuation of bias and injustice.

One year after her acrimonious exit from Google, ethics researcher Timnit Gebru launched an independent institute to study neglected issues in AI.

Tech companies generally try to be (or to appear to be) socially responsible. Would some rather let AI’s negative impacts slide?

Researchers found serious flaws in an influential language dataset, highlighting the need for better documentation of data used in machine learning.

Having dismissed two key researchers, Google restructured its efforts in AI ethics. Marian Croak, an accomplished software engineer and vice president of engineering at Google, will lead a new center of expertise in responsible AI, the company announced.

Some of deep learning’s bedrock datasets came under scrutiny as researchers combed them for built-in biases. Researchers found that popular datasets impart biases against socially marginalized groups to trained models due to the ways the datasets were compiled, labeled, and used.

A new model designed to sharpen images tends to turn some dark faces white, igniting fresh furor over bias in machine learning. Photo Upsampling via Latent Space Exploration (Pulse) generates high-resolution versions of low-resolution images.