Alignment, bias, regulation, and responsible AI
What happens that while training an AI during pre training we make it such that if makes "misaligned behaviour" then we just reduce like ...
What happens that while training an AI during pre training we make it such that if makes "misaligned behaviour" then we just reduce like ...
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![[2602.21773] Easy to Learn, Yet Hard to Forget: Towards Robust Unlearning Under Bias](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This paper discusses the challenges of machine unlearning in the presence of biased data, introducing a novel framework called CUPID to e...
The paper introduces TiMi, a novel approach that enhances time series forecasting by integrating multimodal data through a Mixture of Exp...
This article presents a multimodal machine learning framework for predicting 5-year breast cancer survival, integrating clinical and geno...
The paper introduces a novel attack method, Coherence-Preserving Semantic Injection (CSI), that exploits vulnerabilities in semantic-awar...
The paper introduces WaterVIB, a framework for robust watermarking that utilizes the Variational Information Bottleneck to enhance resili...
This article presents a novel approach to world modeling in AI using Vector Symbolic Architecture (VSA) to enhance generalization and int...
The paper introduces Proximal-IMH, a novel sampling method for Bayesian inverse problems that enhances the efficiency of the Independent ...
The paper 'Defensive Generation' presents a novel approach to creating generative models that are unfalsifiable based on observed data, e...
The paper proposes a new method for evaluating AI models using robust lotteries, addressing limitations of traditional pairwise compariso...
This study evaluates the performance of foundation models in detecting abdominal trauma, revealing that specificity deficits are influenc...
This paper presents a novel approach to monocular normal estimation by reformulating the problem as shading sequence estimation, enhancin...
The paper discusses vulnerabilities in AI control protocols, specifically how Agent-as-a-Proxy attacks can bypass existing monitoring def...
This article analyzes the classification of ChatGPT under the Digital Services Act (DSA), proposing it as a hybrid of search engine and p...
This article explores the gaps in understanding superintelligence misalignment, emphasizing the absence of the human subject and the impl...
The paper presents EARL, an Entropy-Aware Reinforcement Learning framework designed to enhance the reliability of RTL code generation by ...
The paper presents an innovative framework called Truthful Text Summarization (TTS) aimed at enhancing the factual accuracy of multi-sour...
This paper argues for a shift in machine learning fairness research to focus on structural injustice through social determinants, rather ...
This paper explores mechanistic indicators of understanding in large language models (LLMs), proposing a tiered framework to assess their...
This article presents a comprehensive benchmark for electrocardiogram (ECG) time-series analysis, highlighting its unique characteristics...
This paper introduces a novel attack and auditing framework for Vertical Federated Learning (VFL), addressing vulnerabilities in inferenc...
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