The widespread adoption of voice-enabled authentication and audio biometric systems have significantly increased privacy vulnerabilities associated with sensitive speech data. Compliance with privacy regulations such as GDPR's right to be forgotten and India's DPDP Act necessitates targeted and efficient erasure of individual-specific voice signatures from already-trained biometric models. Existing unlearning methods designed for visual data inadequately handle the sequential, temporal, and high-dimensional nature of audio signals, leading to ineffective or incomplete speaker and accent erasure. To address this, we introduce QPAudioEraser, a quantum-inspired audio unlearning framework. Our our-phase approach involves: (1) weight initialization using destructive interference to nullify target features, (2) superposition-based label transformations that obscure class identity, (3) an uncertainty-maximizing quantum loss function, and (4) entanglement-inspired mixing of correlated weights to retain model knowledge. Comprehensive evaluations with ResNet18, ViT, and CNN architectures across AudioMNIST, Speech Commands, LibriSpeech, and Speech Accent Archive datasets validate QPAudioEraser's superior performance. The framework achieves complete erasure of target data (0% Forget Accuracy) while incurring minimal impact on model utility, with a performance degradation on retained data as low as 0.05%. QPAudioEraser consistently surpasses conventional baselines across single-class, multi-class, sequential, and accent-level erasure scenarios, establishing the proposed approach as a robust privacy-preserving solution.

As autonomous agents powered by large language models (LLMs) proliferate in high-stakes domains -- from pharmaceuticals to legal workflows -- the challenge is no longer just intelligence, but verifiability. We introduce TrustTrack, a protocol that embeds structural guarantees -- verifiable identity, policy commitments, and tamper-resistant behavioral logs -- directly into agent infrastructure. This enables a new systems paradigm: trust-native autonomy. By treating compliance as a design constraint rather than post-hoc oversight, TrustTrack reframes how intelligent agents operate across organizations and jurisdictions. We present the protocol design, system requirements, and use cases in regulated domains such as pharmaceutical R&D, legal automation, and AI-native collaboration. We argue that the Cloud -> AI -> Agent -> Trust transition represents the next architectural layer for autonomous systems.

As AI-driven dataspaces become integral to data sharing and collaborative analytics, ensuring privacy, performance, and policy compliance presents significant challenges. This paper provides a comprehensive review of privacy-preserving and policy-aware AI techniques, including Federated Learning, Differential Privacy, Trusted Execution Environments, Homomorphic Encryption, and Secure Multi-Party Computation, alongside strategies for aligning AI with regulatory frameworks such as GDPR and the EU AI Act. We propose a novel taxonomy to classify these techniques based on privacy levels, performance impacts, and compliance complexity, offering a clear framework for practitioners and researchers to navigate trade-offs. Key performance metrics -- latency, throughput, cost overhead, model utility, fairness, and explainability -- are analyzed to highlight the multi-dimensional optimization required in dataspaces. The paper identifies critical research gaps, including the lack of standardized privacy-performance KPIs, challenges in explainable AI for federated ecosystems, and semantic policy enforcement amidst regulatory fragmentation. Future directions are outlined, proposing a conceptual framework for policy-driven alignment, automated compliance validation, standardized benchmarking, and integration with European initiatives like GAIA-X, IDS, and Eclipse EDC. By synthesizing technical, ethical, and regulatory perspectives, this work lays the groundwork for developing trustworthy, efficient, and compliant AI systems in dataspaces, fostering innovation in secure and responsible data-driven ecosystems.

As artificial intelligence and robotics increasingly reshape the global labor market, understanding public perceptions of these technologies becomes critical. We examine how these perceptions have evolved across Latin America, using survey data from the 2017, 2018, 2020, and 2023 waves of the Lati-nobar ometro. Drawing on responses from over 48,000 individuals across 16 countries, we analyze fear of job loss due to artificial intelligence and robotics. Using statistical modeling and latent class analysis, we identify key structural and ideological predictors of concern, with education level and political orientation emerging as the most consistent drivers. Our findings reveal substantial temporal and cross-country variation, with a notable peak in fear during 2018 and distinct attitudinal profiles emerging from latent segmentation. These results offer new insights into the social and structural dimensions of AI anxiety in emerging economies and contribute to a broader understanding of public attitudes toward automation beyond the Global North.

Epistemic injustice related to AI is a growing concern. In relation to machine learning models, epistemic injustice can have a diverse range of sources, ranging from epistemic opacity, the discriminatory automation of testimonial prejudice, and the distortion of human beliefs via generative AI's hallucinations to the exclusion of the global South in global AI governance, the execution of bureaucratic violence via algorithmic systems, and interactions with conversational artificial agents. Based on a proposed general taxonomy of epistemic injustice, this paper first sketches a taxonomy of the types of epistemic injustice in the context of AI, relying on the work of scholars from the fields of philosophy of technology, political philosophy and social epistemology. Secondly, an additional conceptualization on epistemic injustice in the context of AI is provided: generative hermeneutical erasure. I argue that this injustice the automation of 'epistemicide', the injustice done to epistemic agents in their capacity for collective sense-making through the suppression of difference in epistemology and conceptualization by LLMs. AI systems' 'view from nowhere' epistemically inferiorizes non-Western epistemologies and thereby contributes to the erosion of their epistemic particulars, gradually contributing to hermeneutical erasure. This work's relevance lies in proposal of a taxonomy that allows epistemic injustices to be mapped in the AI domain and the proposal of a novel form of AI-related epistemic injustice.

Olaf Op den Camp Integrated Vehicle Safety TNO Helmond, the Netherlands 0000 - 0002 - 6355 - 134X Erwin de Gelder Integrated Vehicle Safety TNO Helmond, the Netherlands 0000 - 0003 - 4260 - 4294 Abstract -- Before introducing an Automated Driving System (ADS) on the road at scale, the manufacturer must conduct some sort of safety assurance. To structure and harmonize the safety assurance process, the UNECE WP.29 Working Party on Automated/Autonomous and Connected Vehicles (GRVA) is developing the New Assessment/Test Method (NATM) that indicates what steps need to be taken for safety assessment of an ADS . In this paper, we will show how to practically conduct safety assessment making use of a scenario database, and what additional steps must be taken to fully operationalize the NATM. In addition, we will elaborate on how the use of scenario databases fits with methods developed in the Horizon Europe projects that focus on safety assessment following the NATM ap proach. A safety assurance process that is conducted by the manufacturer before introducing an Automated Driving System (ADS), intends to assure that the ADS responds appropriately in all situations it is designed for and that the ADS is able to avoid any reasonably foreseeable and reasonably preventable collision s . The information out of the safety assurance process is not only important for manufacturers, but also for authorities that have the responsibility to guard the safety of their citizens in traffic. Safety assurance is most important for consumers (and fle et owners) using an ADS with the expectation that the system is saf e, reliable, and trustworthy . To structure and harmonize this process, t he UNECE WP.29 Working Party on Automated/Autonomous and Connected Vehicles (GRVA) is developing the New Assessment/Test Method (NATM) [1], which is already recognized across many countries (e.g., Japan, South Korea, the EU and the USA).

This paper focuses on the legal compliance challenges of autonomous vehicles in a transnational context. We choose the perspective of designers and try to provide supporting legal reasoning in the design process. Based on argumentation theory, we introduce a logic to represent the basic properties of argument-based practical (normative) reasoning, combined with partial order sets of natural numbers to express priority. Finally, through case analysis of legal texts, we show how the reasoning system we provide can help designers to adapt their design solutions more flexibly in the cross-border application of autonomous vehicles and to more easily understand the legal implications of their decisions.

"Instead of heeding our call to remove its'NSFW' AI chatbot, xAI appears to be doubling down on furthering sexual exploitation by enabling AI videos to create nudity," said Haley McNamara, a senior vice president at the National Center on Sexual Exploitation. "There's no confirmation it won't create pornographic content that resembles a recognizable person. Here's what to know about the new rollout. The latest xAI update arrives amid public concern about deepfakes. Around three-quarters of U.S. adults are in favor of restricting the use of digitally altered videos and images, per a 2019 poll from the Pew Research Center.

The Take It Down Act targets the kind of material usually called "revenge porn": nude images of people, typically but not necessarily sexual, posted without their consent. The phrase is a little misleading, because revenge is just one of many motivations driving it. A more legalese term, precise but bloodless, is "nonconsensual intimate imagery," or NCII. Whatever it is called, the stories of its victims are heartbreaking. Jealous exes post nude selfie images sent to them by their ex-partners.

We really wanted to see what it looks like to be a public defender-first, and try to level that playing field that technology has in many ways exacerbated in past years,