Commercial AI research and deployments are on the rise, a new study highlights. The latest edition of the AI Index, an annual report from Stanford University, documents key trends in the field including the growing importance of private industry and the erosion of U.S. dominance in research.

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.

Datasets for training face recognition models have ballooned in size — while slipping in quality and respect for privacy. In a survey of 130 datasets compiled over the last four decades, researchers traced how the need for increasing quantities of data led researchers to relax their standards.

Facebook’s content moderation algorithms block many advertisements aimed at disabled people. The social media platform’s automated systems regularly reject ads for clothing designed for people with physical disabilities.

U.S. government approval of medical AI products is on the upswing — but information about how such systems were built is largely unavailable. The U.S. Food and Drug Administration (FDA) has approved a a plethora of AI-driven medical systems.

Western governments and institutions struggling to formulate principles of algorithmic fairness tend to focus on issues like race and gender. A new study of AI in India found a different set of key issues.

A model designed to assess medical patients’ pain levels matched the patients’ own reports better than doctors’ estimates did — when the patients were Black.

As AI engineers, we have tools to design and build any technology-based solution we can dream of. But many AI developers don’t consider it their responsibility to address potential negative consequences as a part of this work.

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 database tracks failures of automated systems including machine learning models. The Partnership on AI, a nonprofit consortium of businesses and institutions, launched the AI Incident Database, a searchable collection of reports on the technology’s missteps.

Social biases are well documented in decisions made by supervised models trained on ImageNet’s labels. But they also crept into the output of unsupervised models pretrained on the same dataset.

Will we ever understand what goes on inside the mind of a neural network?The fear: When AI systems go wrong, no one will be able to explain the reasoning behind their decisions.

What if AI requires so much computation that it becomes unaffordable?The fear: Training ever more capable models will become too pricey for all but the richest corporations and government agencies. Rising costs will

Will AI that discriminates based on race, gender, or economic status undermine the public’s confidence in the technology? Seduced by the promise of cost savings and data-driven decision making, organizations will deploy biased systems that end up doing real-world damage.

Synthetic datasets can inherit flaws in the real-world data they’re based on. Startups are working on solutions. Generating synthetic datasets for training machine learning systems is a booming business.