We study the question of fair clustering under the disparate impact doctrine, where each protected class must have approximately equal representation in every cluster. We formulate the fair clustering problem under both the k-center and the k-median objectives, and show that even with two protected classes the problem is challenging, as the optimum solution can violate common conventions--for instance a point may no longer be assigned to its nearest cluster center! En route we introduce the concept of fairlets, which are minimal sets that satisfy fair representation while approximately preserving the clustering objective. We show that any fair clustering problem can be decomposed into first finding good fairlets, and then using existing machinery for traditional clustering algorithms. While finding good fairlets can be NP-hard, we proceed to obtain efficient approximation algorithms based on minimum cost flow. We empirically demonstrate the price of fairness by quantifying the value of fair clustering on real-world datasets with sensitive attributes.

With deepfake video and audio making their way into political campaigns, California enacted its toughest restrictions yet in September: a law prohibiting political ads within 120 days of an election that include deceptive, digitally generated or altered content unless the ads are labeled as "manipulated." On Wednesday, a federal judge temporarily blocked the law, saying it violated the 1st Amendment. Other laws against deceptive campaign ads remain on the books in California, including one that requires candidates and political action committees to disclose when ads are using artificial intelligence to create or substantially alter content. But the preliminary injunction granted against Assembly Bill 2839 means that there will be no broad prohibition against individuals using artificial intelligence to clone a candidate's image or voice and portraying them falsely without revealing that the images or words are fake. The injunction was sought by Christopher Kohls, a conservative commentator who has created a number of deepfake videos satirizing Democrats, including the party's presidential nominee, Vice President Kamala Harris.

The adoption of automated, data-driven decision making in an ever expanding range of applications has raised concerns about its potential unfairness towards certain social groups. In this context, a number of recent studies have focused on defining, detecting, and removing unfairness from data-driven decision systems. However, the existing notions of fairness, based on parity (equality) in treatment or outcomes for different social groups, tend to be quite stringent, limiting the overall decision making accuracy. In this paper, we draw inspiration from the fairdivision and envy-freeness literature in economics and game theory and propose preference-based notions of fairness--given the choice between various sets of decision treatments or outcomes, any group of users would collectively prefer its treatment or outcomes, regardless of the (dis)parity as compared to the other groups. Then, we introduce tractable proxies to design margin-based classifiers that satisfy these preference-based notions of fairness. Finally, we experiment with a variety of synthetic and real-world datasets and show that preference-based fairness allows for greater decision accuracy than parity-based fairness.

One of California's new AI laws, which aims to prevent AI deepfakes related to elections from spreading online, has been blocked a month before the US presidential elections. As TechCrunch and Reason report, Judge John Mendez has issued a preliminary injunction, preventing the state's attorney general from enforcing AB 2839. California Governor Gavin Newsom signed it into law, along with other bills focusing on AI, back in mid-September. "I just signed a bill to make this illegal in the state of California," he wrote. I just signed a bill to make this illegal in the state of California.

Using an AI-powered app to create fake nude pictures of people without their consent violates all sorts of norms, especially when those people are minors. It would not, however, violate California law -- yet. A pair of bills newly signed by Gov. Gavin Newsom outlaw the creation, possession and distribution of sexually charged images of minors even when they're created with computers, not cameras. The measures take effect Jan. 1. The expansion of state prohibitions comes as students are increasingly being victimized by apps that use artificial intelligence either to take a photo of a fully clothed real person and digitally generate a nude body ("undresser" apps) or seamlessly superimpose the image of a person's face onto a nude body from a pornographic video.

Fox News' Eben Brown reports that as election season ramps up, scammers are producing deepfakes that are so sophisticated, they're almost impossible to detect. A federal judge on Wednesday blocked a California bill that outlaws AI-generated "deepfake" content and required the removal of "deceptive content" from social media. The preliminary injunction comes just two weeks after Democratic Gov. Gavin Newsom signed the controversial measure into law, igniting a spat with X owner and Tesla CEO Elon Musk. It also comes roughly a month before Election Day. A spokesperson for Newsom's office warned that deepfakes "threaten the integrity of our elections, and these new laws protect our democracy while preserving free speech -- in a manner no more stringent than those in other states, including deep-red Alabama."

U.S. congressional hearings significantly influence the national economy and social fabric, impacting individual lives. Despite their importance, there is a lack of comprehensive datasets for analyzing these discourses. To address this, we propose the Congress Committee Hearing Dataset (CoCoHD), covering hearings from 1997 to 2024 across 86 committees, with 32,697 records. This dataset enables researchers to study policy language on critical issues like healthcare, LGBTQ+ rights, and climate justice. We demonstrate its potential with a case study on 1,000 energy-related sentences, analyzing the Energy and Commerce Committee's stance on fossil fuel consumption. By fine-tuning pre-trained language models, we create energy-relevant measures for each hearing. Our market analysis shows that natural language analysis using CoCoHD can predict and highlight trends in the energy sector.

Diffusion Models (DMs) have evolved into advanced image generation tools, especially for few-shot fine-tuning where a pretrained DM is fine-tuned on a small set of images to capture specific styles or objects. Many people upload these personalized checkpoints online, fostering communities such as Civitai and HuggingFace. However, model owners may overlook the potential risks of data leakage by releasing their fine-tuned checkpoints. Moreover, concerns regarding copyright violations arise when unauthorized data is used during fine-tuning. In this paper, we ask: "Can training data be extracted from these fine-tuned DMs shared online?" A successful extraction would present not only data leakage threats but also offer tangible evidence of copyright infringement. To answer this, we propose FineXtract, a framework for extracting fine-tuning data. Our method approximates fine-tuning as a gradual shift in the model's learned distribution -- from the original pretrained DM toward the fine-tuning data. By extrapolating the models before and after fine-tuning, we guide the generation toward high-probability regions within the fine-tuned data distribution. We then apply a clustering algorithm to extract the most probable images from those generated using this extrapolated guidance. Experiments on DMs fine-tuned with datasets such as WikiArt, DreamBooth, and real-world checkpoints posted online validate the effectiveness of our method, extracting approximately 20% of fine-tuning data in most cases, significantly surpassing baseline performance.

Case law is instrumental in shaping our understanding of human rights, including the right to adequate housing. The HUDOC database provides access to the textual content of case law from the European Court of Human Rights (ECtHR), along with some metadata. While this metadata includes valuable information, such as the application number and the articles addressed in a case, it often lacks detailed substantive insights, such as the specific issues a case covers. This underscores the need for detailed analysis to extract such information. However, given the size of the database - containing over 40,000 cases - an automated solution is essential. In this study, we focus on the right to adequate housing and aim to build models to detect cases related to housing and eviction issues. Our experiments show that the resulting models not only provide performance comparable to more sophisticated approaches but are also interpretable, offering explanations for their decisions by highlighting the most influential words. The application of these models led to the identification of new cases that were initially overlooked during data collection. This suggests that NLP approaches can be effectively applied to categorise case law based on the specific issues they address.

Drawing on the experiences of facilitators who have overseen 43 games over a four-year period, we illuminate recurring patterns, strategies, and decision-making processes observed during gameplay. Our analysis reveals key strategic considerations about AI development trajectories in this simulated environment, including: the destabilising effects of AI races, the crucial role of international cooperation in mitigating catastrophic risks, the challenges of aligning corporate and national interests, and the potential for rapid, transformative change in AI capabilities. We highlight places where we believe the game has been effective in exposing participants to the complexities and uncertainties inherent in AI governance. Key recurring gameplay themes include the emergence of international agreements, challenges to the robustness of such agreements, the critical role of cybersecurity in AI development, and the potential for unexpected crises to dramatically alter AI trajectories. By documenting these insights, we aim to provide valuable foresight for policymakers, industry leaders, and researchers navigating the complex landscape of AI development and governance.