From the beginning of my course, I've been thinking about what my graduation dress was going to be, says Keerthana Krishnadas. I had to make sure I looked 10 out of 10, I couldn't compromise on that. She is one of hundreds of thousands of university students graduating this summer and, like many, has been figuring out what to wear for a photo marking one of life's milestones - without breaking the bank. You can save some bucks if you thrift your outfit because you're only going to wear it once, says Keerthana. She bought hers second hand.

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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.