The safety probe comes after Waymo did a voluntary software recall late last year addressing the same issue. Waymo has caught the attention of the National Transportation Safety Board as the federal agency launched an official investigation into the company for its robotaxis improperly passing school buses in Austin, Texas. The NTSB said on X that it would examine the interaction between Waymo vehicles and school buses stopped for loading and unloading students. The latest federal probe stems from a preliminary evaluation by the National Highway Traffic Safety Administration that looked into how Waymo reacts to stopped school buses in the Texas city. That report led to Waymo's voluntary software recall in December.

Despite their wide adoption, the biases and unintended behaviors of language models remain poorly understood. In this paper, we identify and characterize a phenomenon never discussed before, which we call semantic leakage, where models leak irrelevant information from the prompt into the generation in unexpected ways. We propose an evaluation setting to detect semantic leakage both by humans and automatically, curate a diverse test suite for diagnosing this behavior, and measure significant semantic leakage in 13 flagship models. We also show that models exhibit semantic leakage in languages besides English and across different settings and generation scenarios. This discovery highlights yet another type of bias in language models that affects their generation patterns and behavior.

Over half a million school buses travel in the United States, flashing their red stop signs. But many drivers pass illegally. Company Bus Patrol has a high-tech solution to a growing problem. VIERA, Fla. - Over half a million school buses travel in the United States, flashing their red stop signs. But many drivers pass illegally.

Pilot program's main goal to deter drivers from speeding past school buses when stop-arm is out The latest breaking updates, delivered straight to your email inbox. Pilot program's main goal to deter drivers from speeding past school buses when stop-arm is out A pilot program at the Sacramento City Unified School District is hoping to improve student safety through the use of artificial intelligence cameras on school buses. The District has partnered with company BusPatrol to put cameras on five school buses to deter drivers from speeding past buses when their stop-arm is out, which is a violation of law. "We use an AI engine named Ava to be able to automatically detect vehicles that illegally pass stopped school buses," BusPatrol CEO Jean Souliere said. Souliere said the cameras on the side of the buses take pictures of the license plates of cars that keep driving.

When the Boston public school system announced new start times last December, some parents found the schedules unacceptable and pushed back. The algorithm used to set these times had been designed by MIT researchers, and about a week later, Kade Crockford, director of the Technology for Liberty Program at the ACLU of Massachusetts, emailed asking me to cosign an op-ed that would call on policymakers to be more thoughtful and democratic when they consider using algorithms to change policies that affect the lives of residents. Kade, who is also a Director's Fellow at the Media Lab and a colleague of mine, is always paying attention to the key issues in digital liberties and is great at flagging things that I should pay attention to. I made a few edits to her draft, and we shipped it off to the Boston Globe, which ran it on December 22, 2017, under the headline "Don't blame the algorithm for doing what Boston school officials asked." In the op-ed, we piled on in criticizing the changes but argued that people shouldn't criticize the algorithm, but rather the city's political process that prescribed the way in which the various concerns and interests would be optimized.

The big yellow school bus, with its plastic seats, rubbery smell, and cool-kids-in-the-back social hierarchies, hasn't gotten a true update in decades. What will happen to such an old-fashioned vehicle when our streets become flooded with driverless cars? We know how service vehicles like delivery vans and city buses will be affected by autonomous tech. What about school buses, which are so vital and also so fraught with concerns over safety? Will parents ever trust an autonomous vehicle enough to allow their children to ride in one with no human supervision?

We describe an application of AI search and information visualization techniques to the problem of school redistricting, in which students are assigned to home schools within a county or school district. This is a multicriteria optimization problem in which competing objectives, such as school capacity, busing costs, and socioeconomic distribution, must be considered. Because of the complexity of the decision-making problem, tools are needed to help end users generate, evaluate, and compare alternative school assignment plans. A key goal of our research is to aid users in finding multiple qualitatively different redistricting plans that represent different trade-offs in the decision space. We present heuristic search methods that can be used to find a set of qualitatively different plans, and give empirical results of these search methods on population data from the school district of Howard County, Maryland. We show the resulting plans using novel visualization methods that we have developed for summarizing and comparing alternative plans.