Early large-scale audio datasets, such as LibriSpeech, were built with hundreds of individual contributors whose voices were instrumental in the development of speech technologies, including audiobooks and voice assistants. Y et, a decade later, these same contributions have exposed voice actors to a range of risks. While existing ethical frameworks emphasize Consent, Credit, and Compensation (C), they do not adequately address the emergent risks involving vocal identities that are increasingly decoupled from context, authorship, and control. Drawing on qualitative interviews with 20 professional voice actors, this paper reveals how synthetic replication of voice without clear provenance or enforceable constraints exposes individuals to both reputational and security threats. Beyond reputational harm, such as re-purposing voice data in erotic content, offensive political messaging, and meme culture, we document concerns about accountability breakdowns when their voice is leveraged to clone voices that are deployed in high-stakes scenarios such as financial fraud, misinformation campaigns, or impersonation scams. In such cases, actors face social and legal fallout without recourse, while very few of them have a legal representative or union protection. To make sense of these shifting dynamics, we introduce the PRAC framework - an expansion of C that foregrounds Privacy, Reputation, Accountability, Consent, Credit, and Compensation as interdependent pillars of data used in the synthetic voice economy. This framework captures how privacy risks are amplified through non-consensual training, how reputational harm arises from decontextualized deployment, and how accountability can be reimagined AI Data ecosystems. We argue that voice, as both a biometric identifier and creative labor, demands governance models that restore creator agency, ensure traceability, and establish enforceable boundaries for ethical reuse.

There was a reasonable amount of discussion about this paper. The author feedback clarified a variety of issues which caused some reviewers to increase their scores, while some of the discussion caused other reviewers to decrease their scores. Although there was one holdout, the majority of the reviewers leaned towards rejection of the paper. However, I believe this is one of the rare cases where the AC should recommend for the PC to accept the paper against the recommendations of the reviewers. The main reason I'm recommending acceptance is due to the broader context and potential impact of the paper.

The manuscript proposes a formalism for computing stochastic estimates of gradients for loss functions. The formalism, referred to as stochastic computation graphs, is very general, applying to models with deterministic and stochastic components, and allowing the computation of gradient estimates for a broad range of models. Methods for deep networks, variational inference, and reinforcement learning are identified as special cases of the proposed framework. The proposed stochastic computation graphs are essentially Bayesian networks which may also contain deterministic nodes, and which are interpreted as encoding distributions over loss functions that are to be minimized. The gradient estimation algorithm extends backpropagation to the partially stochastic case covered by these models, by simply composing score function estimators and pathwise derivative estimators.

This work got mixed reviews: R1 praised the potential impact of such a simple idea being shown to work remarkably well, but other reviewers had significant concerns about the empirical evaluation, which is especially important when the main contribution of the paper is to show that an idea is effective in practice. The reviewers were ultimately unable to reach a consensus about this paper, but all reviewers agreed that the core idea is promising, and R2, R3 and R4 raised their scores in light of the discussion and the author feedback. While the resulting scores still make this a difficult decision overall, I have chosen to recommend acceptance. The main point of discussion was whether the required changes to the manuscript require another review cycle or not. Indeed, the requested changes were quite broad: - demonstrate the effect of the initial variance of the linear layers - compare the model against modern autoencoder variants - compare against vanilla autoencoders with varying latent dimension - demonstrate the effect of the number of linear layers - avoid overclaiming, e.g. about the proposed model working well "with all types of optimizers" - etc.

The theoretical contributions (relationship between time-dependent exogenous intensity to average activity) appear significant, and the use of this result to derive a closed-form control algorithm appears to be a contribution to the field of shaping activities in social (and other) networks modeled as multivariate Hawkes processes. Regarding the clarity of this paper: The paper does not frame itself relative to prior work clearly. Clearly indicating where and how this work is a generalization of prior work [8] would improve clarity of the paper, and highlight the core contribution of handling *time-dependent* exogenous events. Stating that the paper "establishes theoretical foundations of optimal campaigning over social networks where user activities are modeled as multivariate Hawkes processes" does not properly localize the work relative to [8]. This could be improved in part by mentioning [8] in the introduction, and specifically stating that prior work has done optimal control with constant exogenous control, and that this paper addresses multi-stage exogenous intensity.

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Integrating AI into education has the potential to transform the teaching of science and technology courses, particularly in the field of cybersecurity. AI-driven question-answering (QA) systems can actively manage uncertainty in cybersecurity problem-solving, offering interactive, inquiry-based learning experiences. Large language models (LLMs) have gained prominence in AI-driven QA systems, offering advanced language understanding and user engagement. However, they face challenges like hallucinations and limited domain-specific knowledge, which reduce their reliability in educational settings. To address these challenges, we propose CyberRAG, an ontology-aware retrieval-augmented generation (RAG) approach for developing a reliable and safe QA system in cybersecurity education. CyberRAG employs a two-step approach: first, it augments the domain-specific knowledge by retrieving validated cybersecurity documents from a knowledge base to enhance the relevance and accuracy of the response. Second, it mitigates hallucinations and misuse by integrating a knowledge graph ontology to validate the final answer. Experiments on publicly available cybersecurity datasets show that CyberRAG delivers accurate, reliable responses aligned with domain knowledge, demonstrating the potential of AI tools to enhance education.

Expert-driven frameworks for impact assessments (IAs) may inadvertently overlook the effects of AI technologies on the public's social behavior, policy, and the cultural and geographical contexts shaping the perception of AI and the impacts around its use. This research explores the potentials of fine-tuning LLMs on negative impacts of AI reported in a diverse sample of articles from 266 news domains spanning 30 countries around the world to incorporate more diversity into IAs. Our findings highlight (1) the potential of fine-tuned open-source LLMs in supporting IA of AI technologies by generating high-quality negative impacts across four qualitative dimensions: coherence, structure, relevance, and plausibility, and (2) the efficacy of small open-source LLM (Mistral-7B) fine-tuned on impacts from news media in capturing a wider range of categories of impacts that GPT-4 had gaps in covering.