While diffusion models excel at image generation, their growing adoption raises critical concerns around copyright issues and model transparency. Existing attribution methods identify training examples influencing an entire image, but fall short in isolating contributions to specific elements, such as styles or objects, that matter most to stakeholders. To bridge this gap, we introduce \emph{concept-level attribution} via a novel method called \emph{Concept-TRAK}. Concept-TRAK extends influence functions with two key innovations: (1) a reformulated diffusion training loss based on diffusion posterior sampling, enabling robust, sample-specific attribution; and (2) a concept-aware reward function that emphasizes semantic relevance. We evaluate Concept-TRAK on the AbC benchmark, showing substantial improvements over prior methods. Through diverse case studies--ranging from identifying IP-protected and unsafe content to analyzing prompt engineering and compositional learning--we demonstrate how concept-level attribution yields actionable insights for responsible generative AI development and governance.

The app has recently experienced a surge in popularity - as well as criticism from some who claim it is anti-men. Tea lets women check whether potential partners are married or registered sex offenders as well as run reverse image searches to protect against "catfishing", where people use fake online identities. But one of the most controversial aspects of Tea is that it allows women to share information on men they have dated to "avoid red flags" but also highlight those with "green flag" qualities. The company said the breached photos "can in no way be linked to posts within Tea". The firm blocks screenshots so that posts are not shared outside the app.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A California man was federally charged for allegedly scamming more than 2 million from people over popular dating apps by posing as someone who was "financially successful and knowledgeable about investments," prosecutors said. Christopher Earl Lloyd, 39, of Whittier, is now facing a 14-count federal indictment in connection with the alleged scheme he carried out for nearly three years on dating apps such as Tinder, Hinge and Bumble, according to the U.S. Attorney's Office of the Central District of California. "According to the indictment that a federal grand jury returned on July 2, from April 2021 to February 2024, Lloyd used dating apps and websites to befriend and engage in romantic relationships with his victims. Lloyd lied to his victims to give them the impression that he was financially successful and knowledgeable about investments," the Attorney's Office said.

On November 2, 2022, I attended a Google AI event in New York City. One of the themes was responsible AI. As I listened to executives talk about how they aligned their technology with human values, I realized that the malleability of AI models was a double-edged sword. Models could be tweaked to, say, minimize biases, but also to enforce a specific point of view. Governments could demand manipulation to censor unwelcome facts and promote propaganda.

Two summers ago, the Santa Monica-based company behind the popular video game "Call of Duty" sent a letter to a 24-year-old man in Antioch, Tenn., who went by the online handle "Lerggy." Known in real life as Ryan Rothholz, court filings say, he is the creator of "Lergware," hacking software that enabled Call of Duty players to cheat by kicking opponents offline. A lawsuit filed in May against Rothholz and others allegedly involved in the hacking scheme is the latest salvo in years-long campaign by Activision-Blizzard and other companies to rid their games of cheating. The war is being waged in the Central District of California civil courts, but the defendants are scattered across the country and as far away as Australia. An immersive "first-person shooter" game, Call of Duty takes players into simulated, realistic military combat.

Donald Trump's AI summit in Washington this week was a fanfare-filled event catered to the tech elite. The president took the stage on Wednesday evening, as the song God Bless the USA piped over the loudspeakers, and then he decreed: "America must once again be a country where innovators are rewarded with a green light, not strangled with red tape, so they can't move, so they can't breathe." The message was clear – the tech regulatory environment that was once the focus of federal lawmakers is no longer. "I've been watching for many years," Trump continued. I've been a victim of regulation."

As AI becomes prevalent in high-risk domains and decision-making, it is essential to test for potential harms and biases. This urgency is reflected by the global emergence of AI regulations that emphasise fairness and adequate testing, with some mandating independent bias audits. However, procuring the necessary data for fairness testing remains a significant challenge. Particularly in industry settings, legal and privacy concerns restrict the collection of demographic data required to assess group disparities, and auditors face practical and cultural challenges in gaining access to data. Further, internal historical datasets are often insufficiently representative to identify real-world biases. This work focuses on evaluating classifier fairness when complete datasets including demographics are inaccessible. We propose leveraging separate overlapping datasets to construct complete synthetic data that includes demographic information and accurately reflects the underlying relationships between protected attributes and model features. We validate the fidelity of the synthetic data by comparing it to real data, and empirically demonstrate that fairness metrics derived from testing on such synthetic data are consistent with those obtained from real data. This work, therefore, offers a path to overcome real-world data scarcity for fairness testing, enabling independent, model-agnostic evaluation of fairness, and serving as a viable substitute where real data is limited.

Despite the impressive performance of large language models (LLMs) in general domains, they often underperform in specialized domains. Existing approaches typically rely on data synthesis methods and yield promising results by using unlabeled data to capture domain-specific features. However, these methods either incur high computational costs or suffer from performance limitations, while also demonstrating insufficient generalization across different tasks. To address these challenges, we propose AQuilt, a framework for constructing instruction-tuning data for any specialized domains from corresponding unlabeled data, including Answer, Question, Unlabeled data, Inspection, Logic, and Task type. By incorporating logic and inspection, we encourage reasoning processes and self-inspection to enhance model performance. Moreover, customizable task instructions enable high-quality data generation for any task. As a result, we construct a dataset of 703k examples to train a powerful data synthesis model. Experiments show that AQuilt is comparable to DeepSeek-V3 while utilizing just 17% of the production cost. Further analysis demonstrates that our generated data exhibits higher relevance to downstream tasks. Source code, models, and scripts are available at https://github.com/Krueske/AQuilt.

In common law systems, legal professionals such as lawyers and judges rely on precedents to build their arguments. As the volume of cases has grown massively over time, effectively retrieving prior cases has become essential. Prior case retrieval (PCR) is an information retrieval (IR) task that aims to automatically identify the most relevant court cases for a specific query from a large pool of potential candidates. While IR methods have seen several paradigm shifts over the last few years, the vast majority of PCR methods continue to rely on traditional IR methods, such as BM25. The state-of-the-art deep learning IR methods have not been successful in PCR due to two key challenges: i. Lengthy legal text limitation; when using the powerful BERT-based transformer models, there is a limit of input text lengths, which inevitably requires to shorten the input via truncation or division with a loss of legal context information. ii. Lack of legal training data; due to data privacy concerns, available PCR datasets are often limited in size, making it difficult to train deep learning-based models effectively. In this research, we address these challenges by leveraging LLM-based text embedders in PCR. LLM-based embedders support longer input lengths, and since we use them in an unsupervised manner, they do not require training data, addressing both challenges simultaneously. In this paper, we evaluate state-of-the-art LLM-based text embedders in four PCR benchmark datasets and show that they outperform BM25 and supervised transformer-based models.

Efficient scalability of automated driving (AD) is key to reducing costs, enhancing safety, conserving resources, and maximizing impact. However, research focuses on specific vehicles and context, while broad deployment requires scalability across various configurations and environments. Differences in vehicle types, sensors, actuators, but also traffic regulations, legal requirements, cultural dynamics, or even ethical paradigms demand high flexibility of data-driven developed capabilities. In this paper, we address the challenge of scalable adaptation of generic capabilities to desired systems and environments. Our concept follows a two-stage fine-tuning process. In the first stage, fine-tuning to the specific environment takes place through a country-specific reward model that serves as an interface between technological adaptations and socio-political requirements. In the second stage, vehicle-specific transfer learning facilitates system adaptation and governs the validation of design decisions. In sum, our concept offers a data-driven process that integrates both technological and socio-political aspects, enabling effective scalability across technical, legal, cultural, and ethical differences.