Things to Do in L.A. Tap to enable a layout that focuses on the article. Did Kamala Harris just destroy her 2028 chances? Is Gavin Newsom glad she did? One pointed passage in Kamala Harris' new book refers to her longtime frenemy, Gavin Newsom, and his response to her elevation atop the Democratic ticket. This is read by an automated voice.

In 2025, AI is poised to change every aspect of democratic politics--but it won't necessarily be for the worse. India's prime minister, Narendra Modi, has used AI to translate his speeches for his multilingual electorate in real time, demonstrating how AI can help diverse democracies to be more inclusive. AI avatars were used by presidential candidates in South Korea in electioneering, enabling them to provide answers to thousands of voters' questions simultaneously. We are also starting to see AI tools aid fundraising and get-out-the-vote efforts. AI techniques are starting to augment more traditional polling methods, helping campaigns get cheaper and faster data.

Populism is a concept that is often used but notoriously difficult to measure. Common qualitative measurements like holistic grading or content analysis require great amounts of time and labour, making it difficult to quickly scope out which politicians should be classified as populist and which should not, while quantitative methods show mixed results when it comes to classifying populist rhetoric. In this paper, we develop a pipeline to train and validate an automated classification model to estimate the use of populist language. We train models based on sentences that were identified as populist and pluralist in 300 US governors' speeches from 2010 to 2018 and in 45 speeches of presidential candidates in 2016. We find that these models classify most speeches correctly, including 84% of governor speeches and 89% of presidential speeches. These results extend to different time periods (with 92% accuracy on more recent American governors), different amounts of data (with as few as 70 training sentences per category achieving similar results), and when classifying politicians instead of individual speeches. This pipeline is thus an effective tool that can optimise the systematic and swift classification of the use of populist language in politicians' speeches.

Kentucky Gov. Andy Beshear defended suggesting GOP vice-presidential nominee JD Vance go through a rape-induced abortion to understand the need for reproductive rights in an interview on MSNBC. There's really no other choice but to let Trump be Trump. While Kamala Harris rocked the DNC with an unexpected appearance and short speech to the rapturous crowd, Donald Trump continued to attack his new opponent, sometimes in odd ways. He is ignoring public advice from such close Republican allies as Lindsey Graham. "If you have a policy debate, he wins," the senator said on "Meet the Press."

Republican presidential nominee Donald Trump has posted fake images suggesting that pop star Taylor Swift and her legion of fans are backing him in the upcoming United States election. Trump posted the images, all of which appear to be AI-generated deepfakes taken from right-wing social media accounts with a history of sharing misinformation, along with a message saying "I accept!" One image showed smiling Swift fans, known as Swifties, wearing t-shirts reading: "Swifties for Trump". Another depicted Swift dressed as Uncle Sam, a character from a First World War US Army recruitment poster, urging people to vote for Trump. A third showed a fake headline, beneath the tag "satire", suggesting Swift fans turned to Trump after one of the singer's concerts was cancelled in the Austrian capital Vienna earlier this month when it was targeted by hardliners.

Exclusive: 2024 presidential candidate Robert F. Kennedy, Jr. sits down with'The Story's' Martha MacCallum to discuss his election bid. Independent presidential candidate Robert F. Kennedy, Jr. called on President Biden Wednesday to show the American people he has the "cognitive capacity" and "mental acuity" to lead the nation for another four-year term. "I think he [Biden] needs to come out of the White House and show Americans that he has the cognitive capacity, to, and the mental acuity, to handle this job at probably the most challenging time now, at least in recent American history," RFK Jr. told "The Story." "We're facing issues that are existential. We're involved in two wars. We have AI coming down, which is going to change everything, and there's enormous dangers in it," he continued.

With polls showing voters' concerns over Biden's age, there are growing calls for him to prove his mental fitness ahead of a rematch with Trump.Michael Reynolds/EFE/ZUMA The daughter of the once-oldest president, Ronald Reagan, who was 77 when he took office, thinks cognitive tests for presidential candidates would be "a good idea," she said in an interview that aired Sunday. "Just what we know about what age can do, it doesn't always do that, but it would probably be a good idea," Patti Davis said on NBC's Meet the Press, in response to a question from host Kristen Welker about whether she agreed with the prospect. WATCH: When Ronald Reagan was elected at 69, he was the oldest person ever to be elected president. Now his daughter, Patti Davis, says cognitive tests would be a "good idea." Davis: "My father was 77 when he left office after two terms. It seems so young now, doesn't it?"

OpenAI has suspended the developer behind Dean.Bot, a ChatGPT-powered bot designed to impersonate Democratic presidential candidate Dean Phillips to help bolster his campaign, according to The Washington Post. The chatbot was created by AI startup Delphi for the super PAC We Deserve Better, which supports Phillips. Dean.Bot didn't all-out pretend to be Phillips himself; before engaging with Dean.Bot, website visitors would be shown a disclaimer describing the nature of the chatbot. Still, this type of use goes directly against OpenAI's policies. A spokesperson for the company confirmed the developer's suspension in a statement to the Post. It comes just weeks after OpenAI published a lengthy blog post about the measures it's taking to prevent the misuse of its technology ahead of the 2024 elections, specifically citing "chatbots impersonating candidates" as an example of what's not allowed.

Does Donald Trump speak differently from other presidents? If so, in what ways? Are these differences confined to any single medium of communication? To investigate these questions, this paper introduces a novel metric of uniqueness based on large language models, develops a new lexicon for divisive speech, and presents a framework for comparing the lexical features of political opponents. Applying these tools to a variety of corpora of presidential speeches, we find considerable evidence that Trump's speech patterns diverge from those of all major party nominees for the presidency in recent history. Some notable findings include Trump's employment of particularly divisive and antagonistic language targeting of his political opponents and his patterns of repetition for emphasis. Furthermore, Trump is significantly more distinctive than his fellow Republicans, whose uniqueness values are comparably closer to those of the Democrats. These differences hold across a variety of measurement strategies, arise on both the campaign trail and in official presidential addresses, and do not appear to be an artifact of secular time trends.

Echoing the history of search engines and social media content rankings, the advent of large language models (LLMs) has led to a push for increased personalization of model outputs to individual users. In the past, personalized recommendations and ranking systems have been linked to the development of filter bubbles (serving content that may confirm a user's existing biases) and affective polarization (strong negative sentiment towards those with differing views). In this work, we explore how prompting a leading large language model, ChatGPT-3.5, with a user's political affiliation prior to asking factual questions about public figures and organizations leads to differing results. We observe that left-leaning users tend to receive more positive statements about left-leaning political figures and media outlets, while right-leaning users see more positive statements about right-leaning entities. This pattern holds across presidential candidates, members of the U.S. Senate, and media organizations with ratings from AllSides. When qualitatively evaluating some of these outputs, there is evidence that particular facts are included or excluded based on the user's political affiliation. These results illustrate that personalizing LLMs based on user demographics carry the same risks of affective polarization and filter bubbles that have been seen in other personalized internet technologies. This ``failure mode" should be monitored closely as there are more attempts to monetize and personalize these models.