The University of Chicago is banning laptops, tablets and phones for first-year law students in a sweeping AI ban aimed at preserving critical thinking in legal education.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Omar Yaghi, professor at the University of California, Berkeley, speaks during a media conference in Brussels, Oct. 8, 2025, after being one of three scientists awarded the Nobel Prize in chemistry. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search.

Starship Technologies is pulling 1,200 delivery robots from U.S. college campuses to focus on grocery delivery in cities across the United States and Europe.

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Things to Do in L.A. Tap to enable a layout that focuses on the article. Rep. Nancy Pelosi (D-San Francisco) tours the UC Berkeley campus alongside Chancellor Rich Lyons ahead of announcing the Nancy Pelosi Institute for Representative Democracy. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search.

Modern machine learning systems have outgrown their origins as isolated predictive constructs, evolving into complex socio-technical architectures that actively mediate human opportunity. As algorithms increasingly determine access to economic and social opportunities, it has become widely recognized that these systems are deeply embedded with the structural inequalities and prejudices of their environments. The field of algorithmic fairness emerged in response to the growing recognition that models optimized for predictive accuracy can systematically disadvantage marginalized groups. Early mitigation strategies, however, rested on fragile simplifications that limited their effectiveness in complex sociotechnical environments. This thesis identifies and addresses two fundamental limitations of contemporary fairness paradigms: the reliance on deterministic point estimates for auditing and the treatment of individuals as isolated entities devoid of structural context. First, the diagnosis of algorithmic unfairness has traditionally depended on scalar metrics that fail to capture the nuances of real-world deployment. This deterministic approach ignores the high statistical variance inherent in small, intersectional groups, often leading to false alarms or missed detections of bias. Furthermore, standard auditing struggles with the opacity of black-box models, frequently conflating unjustifiable bias with the influence of legitimate features.

Meritocratic systems, from admissions to hiring, aim to impartially reward skill and effort.

Inflation, a tough economy, and AI threats to white-collar jobs have crushed trust in elite schools, creating opportunity for community colleges and certification programs.

This week only, every donation is doubled! Halfway through our Summer Membership Drive, we're still well behind where we need to be. But there's good news: This week, every donation will be doubled up, to $50,000 We need you right now. We need you right now. The wave of booing aimed at AI-pilled commencement speakers signals a sea change in public opinion.

When Did White-Collar Work Start to Look So Bleak? In the nineteen-eighties, an office job promised security and fulfillment. For graduates starting careers today, the prospect is often tinged with dread. The workplace's sense of control can prove illusory--as it did in the era of yuppie-wrought corporate consolidation, and as it does now for graduates entering an economy destabilized by new uncertainties. This spring, across the nation's auditoriums and quadrangles, members of the class of 2026 took their seats to receive remarks from distinguished guests. The graduation speech is a thankless form: generalized, impersonal exhortation/congratulation is almost guaranteed to be forgettable, if all goes well. But this year, on at least a few American campuses, all did not go well. At the University of Arizona, Eric Schmidt, the former C.E.O. of Google, told the crowd that artificial intelligence "will touch every profession, every classroom, every hospital, every laboratory, every person, and every relationship you have," a sweeping promise that landed like a threat.