• Introduces PPO-CIS, a novel Deep Reinforcement Learning (DRL) framework for adaptive toxicity detection in classifier cascades. • Utilizes Proximal Policy Optimization (PPO) to dynamically select classifiers based on sample complexity and system cost constraints. • Proposes a custom reward function balancing classification accuracy, latency, and computational efficiency. • Demonstrates improved performance over static cascades and individual models across two benchmark datasets: Kaggle and ToxiGen. • Achieves significant gains in throughput and accuracy, making the system suitable for real-time content moderation at scale. • Provides a scalable and cost-effective moderation solution for social media platforms facing high data volume and regulatory pressure. Online platforms face growing challenges in moderating harmful user-generated content due to the large volume and rapid pace of interactions. In existing moderation systems, automated tools assist human moderators, yet they often struggle to balance processing efficiency and reliable classification. When moderation fails to detect harmful content quickly and accurately, platforms risk user harm and noncompliance with content safety regulations. This paper proposes an adaptive moderation method named the Proximal Policy Optimization-based Cascaded Inference System (PPO-CIS). The method integrates multiple toxicity classifiers into a cascaded decision architecture guided by deep reinforcement learning. At each step, PPO-CIS selects the next classifier according to the content difficulty and the expected gain in accuracy relative to computational cost. The system enables rapid filtering of benign content and only activates high-capacity models for uncertain cases. PPO-CIS is the first toxicity detection framework to employ Proximal Policy Optimization for real-time optimization of classifier cascades. Experiments on the AugmenToxic and ToxiGen datasets show that PPO-CIS improves detection accuracy by 2.10 percent while increasing processing speed from 42.74 to 384 samples per second compared with static cascade designs. The findings show that adaptive model selection can better shield users from exposure to harmful content while lowering moderation costs. PPO-CIS provides a practical solution for deploying scalable and timely content moderation in fast-moving online environments.

Addressing the challenge of toxic language in online discussions is crucial for the development of effective toxicity detection models. This pioneering work focuses on addressing imbalanced datasets in toxicity detection by introducing a novel approach to augment toxic language data. We create a balanced dataset by instructing fine-tuning of Large Language Models (LLMs) using Reinforcement Learning with Human Feedback (RLHF). Recognizing the challenges in collecting sufficient toxic samples from social media platforms for building a balanced dataset, our methodology involves sentence-level text data augmentation through paraphrasing existing samples using optimized generative LLMs. Leveraging generative LLM, we utilize the Proximal Policy Optimizer (PPO) as the RL algorithm to fine-tune the model further and align it with human feedback. In other words, we start by fine-tuning a LLM using an instruction dataset, specifically tailored for the task of paraphrasing while maintaining semantic consistency. Next, we apply PPO and a reward function, to further fine-tune (optimize) the instruction-tuned LLM. This RL process guides the model in generating toxic responses. We utilize the Google Perspective API as a toxicity evaluator to assess generated responses and assign rewards/penalties accordingly. This approach guides LLMs through PPO and the reward function, transforming minority class samples into augmented versions. The primary goal of our methodology is to create a balanced and diverse dataset to enhance the accuracy and performance of classifiers in identifying instances from the minority class. Utilizing two publicly available toxic datasets, we compared various techniques with our proposed method for generating toxic samples, demonstrating that our approach outperforms all others in producing a higher number of toxic samples. Starting with an initial 16,225 toxic prompts, our method successfully generated 122,951 toxic samples with a toxicity score exceeding 30%. Subsequently, we developed various classifiers using the generated balanced datasets and applied a cost-sensitive learning approach to the original imbalanced dataset. The findings highlight the superior performance of classifiers trained on data generated using our proposed method. These results highlight the importance of employing RL and a data-agnostic model as a reward mechanism for augmenting toxic data, thereby enhancing the robustness of toxicity detection models.

Social media use has transformed communication and made social interaction more accessible. Public microblogs allow people to share and access news through existing and social-media-created social connections and access to public news sources. These benefits also create opportunities for the spread of false information. False information online can mislead people, decrease the benefits derived from social media, and reduce trust in genuine news. We divide false information into two categories: unintentional false information, also known as misinformation; and intentionally false information, also known as disinformation and fake news. Given the increasing prevalence of misinformation, it is imperative to address its dissemination on social media platforms. This survey focuses on six key aspects related to misinformation: 1) clarify the definition of misinformation to differentiate it from intentional forms of false information; 2) categorize proposed approaches to manage misinformation into three types: detection, verification, and mitigation; 3) review the platforms and languages for which these techniques have been proposed and tested; 4) describe the specific features that are considered in each category; 5) compare public datasets created to address misinformation and categorize into prelabeled content-only datasets and those including users and their connections; and 6) survey fact-checking websites that can be used to verify the accuracy of information. This survey offers a comprehensive and unprecedented review of misinformation, integrating various methodological approaches, datasets, and content-, user-, and network-based approaches, which will undoubtedly benefit future research in this field.

Aging infrastructure jeopardizes the safe and efficient delivery of critical services, such as water, transportation and flood management. Cities across the globe are not able to keep up with the pace of crumbling infrastructure and must prioritize the replacement and maintenance of certain assets within available budgets. The lack of resources and mismanagement can have dire consequences, as seen in the cases of water contamination in Walkerton, Ontario and Flint, Michigan. Although strategies exist to manage risks of critical infrastructure failure, they do not fully account for social vulnerability. A review of social vulnerability indices indicates that they are largely applied to better understanding risks related to natural and manmade disaster. More recent studies have also focused on the vulnerabilities amplified by the covid-19 pandemic. This attention on acute, urgent and episodic events can be explained by the greater media and policy attention they receive. However, routine risks as caused by infrastructure deterioration generate substantial economic and health consequences and need to be addressed. Infrastructure failure has been found to disproportionately impact marginalized communities. While certain studies frame infrastructure vulnerability as a dimension of social vulnerability, separating these two concepts can enable a better understanding of the feedback loops between social and infrastructure vulnerability. Accordingly, a layered vulnerability approach is proposed. Five key recommendations are made for developing a social vulnerability framework and index to be applied in water infrastructure management decision-making.

Strategic resilience involves organizational transformation through innovation and a long-term vision, which implies a response to uncertainty and distress. However, organizations continue to adopt managerial methods that assume waste reduction and stable conditions. Those methods seem unrealistic and ineffective because disruptions are frequent and additional resources are often required. Here, we propose a perspective of strategic resilience based on buffer capacity building and cooperation, perhaps contrary to traditional methodologies. We consider lean management the conventional approach and use the aviation industry to illustrate emerging alternatives in practice

As the harms of online toxic language become more apparent, countering online toxic behavior is an essential application of natural language processing. The first step in managing toxic language risk is identification, but algorithmic approaches have themselves demonstrated bias. Texts containing some demographic identity terms such as gay or Black are more likely to be labeled as toxic in existing toxic language detection datasets. In many machine learning models introduced for toxic language detection, non-toxic comments containing minority and marginalized community-specific identity terms were given unreasonably high toxicity scores. To address the challenge of bias in toxic language detection, we propose a two-step training approach. A pretrained language model with a multitask learning objective will mitigate biases in the toxicity classifier prediction. Experiments demonstrate that jointly training the pretrained language model with a multitask objective can effectively mitigate the impacts of unintended biases and is more robust to model bias towards commonly-attacked identity groups presented in datasets without significantly hurting the model’s generalizability.

Sustainable waste management is an objective that is far from our current reach, requiring new paradigms of thought, policy, and technology to achieve. The explosion of new applications of data-driven technologies provides the opportunity to be applied to the challenges of waste management and moving towards circular economy. This paper reviews a broad scope of current applications of data-driven and artificial intelligence in the domain of waste management, as collected from journals, reports, and a survey of business practices. We observed that few existing applications aim to make waste data openly available. Based on this gap, we propose novel areas for research and development to assess the potential of collaborative, open, data- driven circular economy initiatives.

Social media allows people to easily express themselves and spread information online. This is a boon to self-expression and communication but has allowed for misinformation to flourish as well. It may be difficult to differentiate facts from misleading opinions. Automatic fact-checking has the potential to reduce the spread of misinformation while browsing. Multiple potential approaches to implementing fact-checking software have been explored. One approach is to detect the information’s origin and evaluate if it is a valid primary source. Most existing methods to model the spread of information online require extensive computational resources and time to train a deep-learning algorithm, as well as a high-level representation of the propagation of the content. In addition, these methods are mainly used to classify and verify the information itself rather than the information’s provenance. The Hawkes process makes it possible to evaluate and model information spread tendencies and map out the source of the information by comparing the intensity of shared posts over time. 1000 posts of 3 blog pages on Reddit were scraped from the Internet to test if the modified Hawkes process can detect which page is influenced by which. The Hawkes process was able to distinguish the influenced, the influencer and the control blog page. Therefore, the Hawkes process may be used to identify the primary sources of information. Future research may need to compare the accuracy and precision of this process compared to other methods.

Ongoing international policy discussions on the regulation of autonomous weapons have advanced the concept of “meaningful human control” as an ethical benchmark for weapons systems. In academic discourse, ethical objections to the development and use of autonomous weapons systems include arguments based in feasibility, accountability, human dignity, and established rules for armed combat. This article situates current policy discussions on autonomous weapons systems, and Canadian policy in particular, with respect to the ethical discourse around such systems. We ask how the idea of meaningful human control intersects with various ethical objections to autonomous weapons, and propose a set of questions for policymakers.

Developing a machine that can hold an engaging conversation with a human is one of the main challenges in designing a dialogue system in the field of natural language processing. Responses generated by neural conversational models with log-likelihood training methods tend to lack informativeness and diversity. We address the limitation of log-likelihood training in dialogue generation models, and we present the Reinforce Transformer decoder model, our new approach for training the Transformer decoder based conversational model, which incorporates proximal policy optimization techniques from re-inforcement learning with the Transformer decoder architecture. We specifically examine the use of our proposed model for multi-turn dialogue response generation in a real word human to a human dataset. To verify the effectiveness of our proposed framework, we evaluate our model on the Reddit dialogues data, which is a real word human to a human dataset. Experiments show that our proposed response generating model in a dialogue achieves significant improvement over recurrent sequence-to-sequence models and also the state of the art Transformer based dialogue generation models based on diversity and relevance evaluation metrics.