Things to Do in L.A. Darian Orduno holds her baby, Gael, as her husband, Antonio, looks on during a town hall meeting at the Episcopal Church of the Savior in Hanford, Calif., which was convened in opposition to a possible data center proposal at the county fairgrounds. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search. Data centers are becoming the political flash point of the midterms as candidates race to respond to tanking public opinion.

"AI-generated" can make it sound as though someone typed a sentence into a machine and came back five minutes later to find a finished movie. Fairground's catalog shows why that description can be too simple. Take Lost Garden: The Awakening of the Lantern Knight. According to its Fairground page, creator Frank Houbre wrote the world, characters, mythology, emotional arc and screenplay himself. AI tools were used mainly for animation and visual production, with other tools helping create voices and music before the episode was assembled in conventional video-editing software.

A new channel on Roku's TV service offers all-you-can-eat AI programming. It is 12:04 p.m., and I've been watching AI slop for the past three hours. Before me on the screen, a man is playing trumpet in fog. He's been fired (bad market for trumpet players) but then, at last, he lands an interview. Trumpet Man sits at a large desk across from an obese fellow who asks how good he is with a mop.

As machine learning (ML) systems are increasingly adopted in high-stakes decision-making domains, ensuring fairness in their outputs has become a central challenge. At the core of fair ML research are the datasets used to investigate bias and develop mitigation strategies. Yet, much of the existing work relies on a narrow selection of datasets--often arbitrarily chosen, inconsistently processed, and lacking in diversity--undermining the generalizability and reproducibility of results. To address these limitations, we present FairGround: a unified framework, data corpus, and Python package aimed at advancing reproducible research and critical data studies in fair ML classification. FairGround currently comprises 44 tabular datasets, each annotated with rich fairness-relevant metadata. Our accompanying Python package standardizes dataset loading, preprocessing, transformation, and splitting, streamlining experimental workflows. By providing a diverse and well-documented dataset corpus along with robust tooling, FairGround enables the development of fairer, more reliable, and more reproducible ML models. All resources are publicly available to support open and collaborative research.