Things to Do in L.A. Tap to enable a layout that focuses on the article. Porn advertisers target California secretary of state's website The state of California's elections and business website appears to be hosting pornography and cash apps as seen through a web search on Dec. 4, 2025. This is read by an automated voice. Please report any issues or inconsistencies here . The California secretary of state's website appears to have been compromised with advertisements for pornography and cash apps.

Face Check will scan new members' faces to ensure they don't match existing profiles. The move comes as romance scams continue to proliferate, with billions lost over the last decade. On Wednesday, Tinder announced that it was rolling out a mandatory facial verification tool for new users in the US to help combat the spread of fake profiles and weed out "bad actors." Tinder claims its mandatory facial integration feature, called Face Check, is a first for a major dating app. During the sign up process, new members complete a "liveness check" by taking a short video selfie within the app.

Detecting bad actors is critical to ensure the safety and integrity of internet platforms. Several deep learning-based models have been developed to identify such users. These models should not only accurately detect bad actors, but also be robust against adversarial attacks that aim to evade detection. However, past deep learning-based detection models do not meet the robustness requirement because they are sensitive to even minor changes in the input sequence. To address this issue, we focus on (1) improving the model understanding capability and (2) enhancing the model knowledge such that the model can recognize potential input modifications when making predictions. To achieve these goals, we create a novel transformer-based classification model, called ROBAD (RObust adversary-aware local-global attended Bad Actor Detection model), which uses the sequence of user posts to generate user embedding to detect bad actors. Particularly, ROBAD first leverages the transformer encoder block to encode each post bidirectionally, thus building a post embedding to capture the local information at the post level. Next, it adopts the transformer decoder block to model the sequential pattern in the post embeddings by using the attention mechanism, which generates the sequence embedding to obtain the global information at the sequence level. Finally, to enrich the knowledge of the model, embeddings of modified sequences by mimicked attackers are fed into a contrastive-learning-enhanced classification layer for sequence prediction. In essence, by capturing the local and global information (i.e., the post and sequence information) and leveraging the mimicked behaviors of bad actors in training, ROBAD can be robust to adversarial attacks. Extensive experiments on Yelp and Wikipedia datasets show that ROBAD can effectively detect bad actors when under state-of-the-art adversarial attacks.

While Artificial Intelligence (AI) is not a new field, recent developments, especially with the release of generative tools like ChatGPT, have brought it to the forefront of the minds of industry workers and academic folk alike. There is currently much talk about AI and its ability to reshape many everyday processes as we know them through automation. It also allows users to expand their ideas by suggesting things they may not have thought of on their own and provides easier access to information. However, not all of the changes this technology will bring or has brought so far are positive; this is why it is extremely important for all modern people to recognize and understand the risks before using these tools and allowing them to cause harm. This work takes a position on better understanding many equity concerns and the spread of misinformation that result from new AI, in this case, specifically ChatGPT and deepfakes, and encouraging collaboration with law enforcement, developers, and users to reduce harm. Considering many academic sources, it warns against these issues, analyzing their cause and impact in fields including healthcare, education, science, academia, retail, and finance. Lastly, we propose a set of future-facing guidelines and policy considerations to solve these issues while still enabling innovation in these fields, this responsibility falling upon users, developers, and government entities.

Reliance on spurious correlations (shortcuts) has been shown to underlie many of the successes of language models. Previous work focused on identifying the input elements that impact prediction. We investigate how shortcuts are actually processed within the model's decision-making mechanism. We use actor names in movie reviews as controllable shortcuts with known impact on the outcome. We use mechanistic interpretability methods and identify specific attention heads that focus on shortcuts. These heads gear the model towards a label before processing the complete input, effectively making premature decisions that bypass contextual analysis. Based on these findings, we introduce Head-based Token Attribution (HTA), which traces intermediate decisions back to input tokens. We show that HTA is effective in detecting shortcuts in LLMs and enables targeted mitigation by selectively deactivating shortcut-related attention heads.

Samantha posted her story on TikTok and shared the scenario on a private Facebook group; many women responded--including her date's wife. Ultimately, as a result of this conversation, Samantha decided to report his profile to Hinge. The next day, the company contacted her to let her know it would be deleting his profile. Mandy and Samantha were pleased with Bumble's and Hinge's swift action to take down the profiles of the men they had matched with--but the experience was indelible. Neither of them plans to use dating apps again.

Machine Learning and data mining techniques (i.e. supervised and unsupervised techniques) are used across domains to detect user safety violations. Examples include classifiers used to detect whether an email is spam or a web-page is requesting bank login information. However, existing ML/DM classifiers are limited in their ability to understand natural languages w.r.t the context and nuances. The aforementioned challenges are overcome with the arrival of Gen-AI techniques, along with their inherent ability w.r.t translation between languages, fine-tuning between various tasks and domains. In this manuscript, we provide a comprehensive overview of the various work done while using Gen-AI techniques w.r.t user safety. In particular, we first provide the various domains (e.g. phishing, malware, content moderation, counterfeit, physical safety) across which Gen-AI techniques have been applied. Next, we provide how Gen-AI techniques can be used in conjunction with various data modalities i.e. text, images, videos, audio, executable binaries to detect violations of user-safety. Further, also provide an overview of how Gen-AI techniques can be used in an adversarial setting. We believe that this work represents the first summarization of Gen-AI techniques for user-safety.

California Gov. Gavin Newsom has vetoed bill SB 1047, which aims to prevent bad actors from using AI to cause "critical harm" to humans. The California state assembly passed the legislation by a margin of 41-9 on August 28, but several organizations including the Chamber of Commerce had urged Newsom to veto the bill. In his veto message on Sept. 29, Newsom said the bill is "well-intentioned" but "does not take into account whether an AI system is deployed in high-risk environments, involves critical decision-making or the use of sensitive data. Instead, the bill applies stringent standards to even the most basic functions - so long as a large system deploys it." SB 1047 would have made the developers of AI models liable for adopting safety protocols that would stop catastrophic uses of their technology.

AI-generated content doesn't seem to have swayed recent European elections But there's still a risk it could in the future, say researchers. AI-generated falsehoods and deepfakes seem to have had no effect on election results in the UK, France, and the European Parliament this year, according to new research. Since the beginning of the generative-AI boom, there has been widespread fear that AI tools could boost bad actors' ability to spread fake content with the potential to interfere with elections or even sway the results. Such worries were particularly heightened this year, when billions of people were expected to vote in over 70 countries. Those fears seem to have been unwarranted, says Sam Stockwell, the researcher at the Alan Turing Institute who conducted the study . He focused on three elections over a four-month period from May to August 2024, collecting data on public reports and news articles on AI misuse.

This document focuses on the threats, especially near-term threats, that Artificial Intelligence (AI) brings to society. Most of the threats discussed here can result from any algorithmic process, not just AI; in addition, defining AI is notoriously difficult. For both reasons, it is important to think of "A+AI": Algorithms and Artificial Intelligence. In addition to the threats, this paper discusses countermeasures to them, and it includes a table showing which countermeasures are likely to mitigate which threats. Thoughtful governance could manage the risks without seriously impeding progress; in fact, chances are it would accelerate progress by reducing the social chaos that would otherwise be likely.