The Detroit Police Department has to adopt new rules curbing its reliance on facial recognition technology after the city reached a settlement this week with Robert Williams, a Black man who was wrongfully arrested in 2020 due to a false face match. It's not an all-out ban on the technology, though, and the court's jurisdiction to enforce the agreement only extends four years. Under the new restrictions, which the ACLU is calling the strongest such policies for law enforcement in the country, police cannot make arrests based solely on facial recognition results or conduct a lineup based only on facial recognition leads. Williams was arrested after facial recognition technology flagged his expired driver's license photo as a possible match for the identity of an alleged shoplifter, which police then used to construct a photo lineup. He was arrested at his home, in front of his family, which he says "completely upended my life."

After a year mostly spent at home channeling Marie Kondo, I bet you're like me, and every drawer, closet, and room is organized. Now, let's tackle your digital life. Look yourself up, and I bet you will find a lot of results you want to remove. Tap or click for insider tricks to make embarrassing, outdated, or personal info (including the Google Street View of your home) vanish from the internet. While you're cleaning things up, tell your digital assistants – looking at you, Siri and Alexa – to butt out.

The next time Waymo talks about its fully autonomous driving technology, you won't see the Alphabet subsidiary use the term "self-driving." That's because the company wants to differentiate the tech it's working on from the semi-autonomous driver-assistance systems that frequently and incorrectly get grouped under the label. "It may seem like a small change, but it's an important one, because precision in language matters and could save lives," the company said. "Unfortunately, we see that some automakers use the term'self-driving' in an inaccurate way, giving consumers and the general public a false impression of the capabilities of driver assist (not fully autonomous) technology… Coalescing around standard terminology will not just prevent misunderstanding and confusion, it will also save lives." In practical terms, one of the first expressions you'll see come out of this change is Waymo's rebranded public education campaign, which is now known as Let's Talk Autonomous Driving.

An artificial intelligence tool Google provides to developers won't add gender labels to images anymore, saying a person's gender can't be determined just by how they look in a photo, Business Insider reports. The company emailed developers today about the change to its widely used Cloud Vision API tool, which uses AI to analyze images and identify faces, landmarks, explicit content, and other recognizable features. Instead of using "man" or "woman" to identify images, Google will tag such images with labels like "person," as part of its larger effort to avoid instilling AI algorithms with human bias. In the email to developers announcing the change, Google cited its own AI guidelines, Business Insider reports. "Given that a person's gender cannot be inferred by appearance, we have decided to remove these labels in order to align with the Artificial Intelligence Principles at Google, specifically Principle #2: Avoid creating or reinforcing unfair bias."

A year ago, the AI Now Institute released its inaugural report on the near-future social and economic consequences of AI, drawing on input from a diverse expert panel representing a spectrum of disciplines; now they've released a followup, with ten clear recommendations for AI implementations in the public and private sector. The first of these is "Core public agencies, such as those responsible for criminal justice, healthcare, welfare, and education (e.g "high stakes" domains) should no longer use'black box' AI and algorithmic systems." The remaining recommendations deal with operational details, like examining training data for bias and validating the performance of the models to ensure that they aren't misfiring; and areas where work needs to be done, like evaluation of the impact of AI on hiring and HR, setting data-set quality standards; bringing cross-disciplinary expertise to bias evaluation; and the active inclusion of women, minorities and other marginalized populations in systems design and evaluation. This includes the unreviewed or unvalidated use of pre-trained models, AI systems licensed from third party vendors, and algorithmic processes created in-house. The use of such systems by public agencies raises serious due process concerns, and at a minimum such systems should be available for public auditing, testing, and review, and subject to accountability standards.