As governments worldwide mull their AI strategies and policies, the Biden administration called for a “bill of rights” to mitigate adverse consequences.What’s new: Top advisors to the U.S. president announced a plan to issue rules

The emerging generation of trillion-parameter models needs datasets of billions of examples, but the most readily available source of examples on that scale — the web — is polluted with bias and antisocial expressions. A new study examines the issue.

Human rights officials called for limits on some uses of AI.What’s new: Michelle Bachelet, the UN High Commissioner for Human Rights, appealed to the organization’s member states to suspend certain

A new study examines a major strain of recent research: huge models pretrained on immense quantities of uncurated, unlabeled data and then fine-tuned on a smaller, curated corpus.

Researchers from Emory University, MIT, Purdue University, and other institutions found that deep learning systems trained to interpret x-rays and CT scans also were able to identify their subjects as Asian, Black, or White.

Compression methods like parameter pruning and quantization can shrink neural networks for use in devices like smartphones with little impact on accuracy — but they also exacerbate a network’s bias.

A U.S. government watchdog agency called for stronger face recognition protocols for federal agencies. An audit found that, while many employ face recognition, they may not know where it came from, how it’s being used, or the hazards involved.

An independent test found flaws in AI systems designed to evaluate job applicants. MyInterview and Curious Thing, which automate job interviews, gave a candidate who spoke only in German high marks on English proficiency, according to MIT Technology Review.

Many people both outside and inside the tech industry believe that AI will serve mostly to boost profits and monitor people — without regard for negative consequences.

Few companies that use AI understand the ethical issues it raises. While many companies are ramping up investments in AI, few look for and correct social biases in their models, according to a report by the credit-scoring company Fico.

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

The European Union proposed sweeping restrictions on AI technologies and applications. The executive arm of the 27-nation EU published draft rules that aim to regulate, and in some cases ban, a range of AI systems.

Police are increasingly able to track motor vehicles throughout the U.S. using a network of AI-powered cameras — many owned by civilians. Flock, which sells automatic license plate readers is encouraging enforcers to use its network to monitor cars and trucks outside their jurisdiction.

Facebook’s management obstructed the architect of its recommendation algorithms from mitigating their negative social impact. The social network focused on reining in algorithmic bias against particular groups of users at the expense of efforts to reduce disinformation.

Auditing is a critical technique in the effort to build fair and equitable AI systems. But current auditing methods may not be up to the task. There’s no consensus on how AI should be audited, whether audits should be mandatory, and what to do with their results.