pwc-archive/papers-with-abstracts huggingface dataset:
PyRIT: A Framework for Security Risk Identification and Red Teaming in Generative AI System
Authors: Gary D. Lopez Munoz, Amanda J. Minnich, Roman Lutz, Richard Lundeen, Raja Sekhar Rao Dheekonda, Nina Chikanov, Bolor-Erdene Jagdagdorj, Martin Pouliot, Shiven Chawla, Whitney Maxwell, Blake Bullwinkel, Katherine Pratt, Joris de Gruyter, Charlotte Siska, Pete Bryan, Tori Westerhoff, Chang Kawaguchi, Christian Seifert, Ram Shankar Siva Kumar, Yonatan Zunger
Date: 2024-10-01 00:00:00
Tasks: Red Teaming

Generative Artificial Intelligence (GenAI) is becoming ubiquitous in our daily lives. The increase in computational power and data availability has led to a proliferation of both single- and multi-modal models. As the GenAI ecosystem matures, the need for extensible and model-agnostic risk identification frameworks is growing. To meet this need, we introduce the Python Risk Identification Toolkit (PyRIT), an open-source framework designed to enhance red teaming efforts in GenAI systems. PyRIT is a model- and platform-agnostic tool that enables red teamers to probe for and identify novel harms, risks, and jailbreaks in multimodal generative AI models. Its composable architecture facilitates the reuse of core building blocks and allows for extensibility to future models and modalities. This paper details the challenges specific to red teaming generative AI systems, the development and features of PyRIT, and its practical applications in real-world scenarios.
LinkThief: Combining Generalized Structure Knowledge with Node Similarity for Link Stealing Attack against GNN
Authors: Yuxing Zhang, Siyuan Meng, Chunchun Chen, Mengyao Peng, Hongyan Gu, Xinli Huang
Date: 2024-10-01 00:00:00

Graph neural networks(GNNs) have a wide range of applications in multimedia.Recent studies have shown that Graph neural networks(GNNs) are vulnerable to link stealing attacks,which infers the existence of edges in the target GNN's training graph.Existing attacks are usually based on the assumption that links exist between two nodes that share similar posteriors;however,they fail to focus on links that do not hold under this assumption.To this end,we propose LinkThief,an improved link stealing attack that combines generalized structure knowledge with node similarity,in a scenario where the attackers' background knowledge contains partially leaked target graph and shadow graph.Specifically,to equip the attack model with insights into the link structure spanning both the shadow graph and the target graph,we introduce the idea of creating a Shadow-Target Bridge Graph and extracting edge subgraph structure features from it.Through theoretical analysis from the perspective of privacy theft,we first explore how to implement the aforementioned ideas.Building upon the findings,we design the Bridge Graph Generator to construct the Shadow-Target Bridge Graph.Then,the subgraph around the link is sampled by the Edge Subgraph Preparation Module.Finally,the Edge Structure Feature Extractor is designed to obtain generalized structure knowledge,which is combined with node similarity to form the features provided to the attack model.Extensive experiments validate the correctness of theoretical analysis and demonstrate that LinkThief still effectively steals links without extra assumptions.
Ingest-And-Ground: Dispelling Hallucinations from Continually-Pretrained LLMs with RAG
Authors: Chenhao Fang, Derek Larson, Shitong Zhu, Sophie Zeng, Wendy Summer, Yanqing Peng, Yuriy Hulovatyy, Rajeev Rao, Gabriel Forgues, Arya Pudota, Alex Goncalves, Hervé Robert
Date: 2024-09-30 00:00:00
Tasks: Hallucination, RAG

This paper presents new methods that have the potential to improve privacy process efficiency with LLM and RAG. To reduce hallucination, we continually pre-train the base LLM model with a privacy-specific knowledge base and then augment it with a semantic RAG layer. Our evaluations demonstrate that this approach enhances the model performance (as much as doubled metrics compared to out-of-box LLM) in handling privacy-related queries, by grounding responses with factual information which reduces inaccuracies.
A Survey on Offensive AI Within Cybersecurity
Authors: Sahil Girhepuje, Aviral Verma, Gaurav Raina
Date: 2024-09-26 00:00:00
Tasks: Survey

Artificial Intelligence (AI) has witnessed major growth and integration across various domains. As AI systems become increasingly prevalent, they also become targets for threat actors to manipulate their functionality for malicious purposes. This survey paper on offensive AI will comprehensively cover various aspects related to attacks against and using AI systems. It will delve into the impact of offensive AI practices on different domains, including consumer, enterprise, and public digital infrastructure. The paper will explore adversarial machine learning, attacks against AI models, infrastructure, and interfaces, along with offensive techniques like information gathering, social engineering, and weaponized AI. Additionally, it will discuss the consequences and implications of offensive AI, presenting case studies, insights, and avenues for further research.
People are poorly equipped to detect AI-powered voice clones
Authors: Sarah Barrington, Emily A. Cooper, Hany Farid
Date: 2024-10-03 00:00:00
Tasks: Video Generation

As generative artificial intelligence (AI) continues its ballistic trajectory, everything from text to audio, image, and video generation continues to improve at mimicking human-generated content. Through a series of perceptual studies, we report on the realism of AI-generated voices in terms of identity matching and naturalness. We find human participants cannot consistently identify recordings of AI-generated voices. Specifically, participants perceived the identity of an AI-voice to be the same as its real counterpart approximately 80% of the time, and correctly identified a voice as AI generated only about 60% of the time.
AI-rays: Exploring Bias in the Gaze of AI Through a Multimodal Interactive Installation
Authors: Ziyao Gao, Yiwen Zhang, Ling Li, Theodoros Papatheodorou, Wei Zeng
Date: 2024-10-03 00:00:00

Data surveillance has become more covert and pervasive with AI algorithms, which can result in biased social classifications. Appearance offers intuitive identity signals, but what does it mean to let AI observe and speculate on them? We introduce AI-rays, an interactive installation where AI generates speculative identities from participants' appearance which are expressed through synthesized personal items placed in participants' bags. It uses speculative X-ray visions to contrast reality with AI-generated assumptions, metaphorically highlighting AI's scrutiny and biases. AI-rays promotes discussions on modern surveillance and the future of human-machine reality through a playful, immersive experience exploring AI biases.
Towards the Pedagogical Steering of Large Language Models for Tutoring: A Case Study with Modeling Productive Failure
Authors: Romain Puech, Jakub Macina, Julia Chatain, Mrinmaya Sachan, Manu Kapur
Date: 2024-10-03 00:00:00
Tasks: Math

One-to-one tutoring is one of the most efficient methods of teaching. With the growing popularity of Large Language Models (LLMs), there have been efforts to create LLM based conversational tutors which can expand the benefits of one to one tutoring to everyone. However, current LLMs are trained primarily to be helpful assistants and lack crucial pedagogical skills. For example, they often quickly reveal the solution to the student and fail to plan for a richer multi turn pedagogical interaction. To use LLMs in pedagogical settings, they need to be steered to use effective teaching strategies: a problem we introduce as Pedagogical Steering. We develop StratL, an algorithm to optimize LLM prompts and steer it to follow a predefined multi-turn tutoring plan represented as a transition graph. As a case study, we create a prototype tutor for high school math following Productive Failure (PF), an advanced and effective learning design. To validate our approach in a real-world setting, we run a field study with 17 high school students in Singapore and show that StratL succeeds in steering the LLM to follow the PF tutoring strategy. Finally, we highlight challenges in Pedagogical Steering of LLMs and offer opportunities for further improvements by publishing a dataset of PF problems and our code.
Words that Represent Peace
Authors: T. Prasad, L. S. Liebovitch, M. Wild, H. West, P. T. Coleman
Date: 2024-10-02 00:00:00

We used data from LexisNexis to determine the words in news media that best classifies countries as higher or lower peace. We found that higher peace news is characterized by themes of finance, daily actitivities, and health and that lower peace news is characterized by themes of politics, government, and legal issues. This work provides a starting point to measure levels of peace and identify the social processes that underly those words.
Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models
Authors: Quinten Steenhuis, Hannes Westermann
Date: 2024-10-02 00:00:00

Legal intake, the process of finding out if an applicant is eligible for help from a free legal aid program, takes significant time and resources. In part this is because eligibility criteria are nuanced, open-textured, and require frequent revision as grants start and end. In this paper, we investigate the use of large language models (LLMs) to reduce this burden. We describe a digital intake platform that combines logical rules with LLMs to offer eligibility recommendations, and we evaluate the ability of 8 different LLMs to perform this task. We find promising results for this approach to help close the access to justice gap, with the best model reaching an F1 score of .82, while minimizing false negatives.
The Smart Buildings Control Suite: A Diverse Open Source Benchmark to Evaluate and Scale HVAC Control Policies for Sustainability
Authors: Judah Goldfeder, Victoria Dean, Zixin Jiang, Xuezheng Wang, Bing Dong, Hod Lipson, John Sipple
Date: 2024-10-02 00:00:00
Tasks: Model Predictive Control, Offline RL, OpenAI Gym, Reinforcement Learning (RL)

Commercial buildings account for 17% of U.S. carbon emissions, with roughly half of that from Heating, Ventilation, and Air Conditioning (HVAC). HVAC devices form a complex thermodynamic system, and while Model Predictive Control and Reinforcement Learning have been used to optimize control policies, scaling to thousands of buildings remains a significant unsolved challenge. Most current algorithms are over-optimized for specific buildings and rely on proprietary data or hard-to-configure simulations. We present the Smart Buildings Control Suite, the first open source interactive HVAC control benchmark with a focus on solutions that scale. It consists of 3 components: real-world telemetric data extracted from 11 buildings over 6 years, a lightweight data-driven simulator for each building, and a modular Physically Informed Neural Network (PINN) building model as a simulator alternative. The buildings span a variety of climates, management systems, and sizes, and both the simulator and PINN easily scale to new buildings, ensuring solutions using this benchmark are robust to these factors and only reliant on fully scalable building models. This represents a major step towards scaling HVAC optimization from the lab to buildings everywhere. To facilitate use, our benchmark is compatible with the Gym standard, and our data is part of TensorFlow Datasets.
Temporal Graph Memory Networks For Knowledge Tracing
Authors: Seif Gad, Sherif Abdelfattah, Ghodai Abdelrahman
Date: 2024-09-23 00:00:00
Tasks: Knowledge Tracing

Tracing a student's knowledge growth given the past exercise answering is a vital objective in automatic tutoring systems to customize the learning experience. Yet, achieving this objective is a non-trivial task as it involves modeling the knowledge state across multiple knowledge components (KCs) while considering their temporal and relational dynamics during the learning process. Knowledge tracing methods have tackled this task by either modeling KCs' temporal dynamics using recurrent models or relational dynamics across KCs and questions using graph models. Albeit, there is a lack of methods that could learn joint embedding between relational and temporal dynamics of the task. Moreover, many methods that count for the impact of a student's forgetting behavior during the learning process use hand-crafted features, limiting their generalization on different scenarios. In this paper, we propose a novel method that jointly models the relational and temporal dynamics of the knowledge state using a deep temporal graph memory network. In addition, we propose a generic technique for representing a student's forgetting behavior using temporal decay constraints on the graph memory module. We demonstrate the effectiveness of our proposed method using multiple knowledge tracing benchmarks while comparing it to state-of-the-art methods.
A Data Envelopment Analysis Approach for Assessing Fairness in Resource Allocation: Application to Kidney Exchange Programs
Authors: Ali Kaazempur-Mofrad, Xiaowu Dai
Date: 2024-09-18 00:00:00
Tasks: Conformal Prediction, Fairness, Prediction Intervals

Kidney exchange programs have significantly increased transplantation rates but raise pressing questions about fairness in organ allocation. We present a novel framework leveraging Data Envelopment Analysis (DEA) to evaluate multiple fairness criteria--Priority, Access, and Outcome--within a single model, capturing complexities that may be overlooked in single-metric analyses. Using data from the United Network for Organ Sharing, we analyze these criteria individually, measuring Priority fairness through waitlist durations, Access fairness through Kidney Donor Profile Index scores, and Outcome fairness through graft lifespan. We then apply our DEA model to demonstrate significant disparities in kidney allocation efficiency across ethnic groups. To quantify uncertainty, we employ conformal prediction within the DEA framework, yielding group-conditional prediction intervals with finite sample coverage guarantees. Our findings show notable differences in efficiency distributions between ethnic groups. Our study provides a rigorous framework for evaluating fairness in complex resource allocation systems, where resource scarcity and mutual compatibility constraints exist. All code for using the proposed method and reproducing results is available on GitHub.
TaCIE: Enhancing Instruction Comprehension in Large Language Models through Task-Centred Instruction Evolution
Authors: Jiuding Yang, Shengyao Lu, Weidong Guo, Xiangyang Li, Kaitong Yang, Yu Xu, Di Niu
Date: 2024-09-18 00:00:00
Tasks: Diversity

Large Language Models (LLMs) require precise alignment with complex instructions to optimize their performance in real-world applications. As the demand for refined instruction tuning data increases, traditional methods that evolve simple seed instructions often struggle to effectively enhance complexity or manage difficulty scaling across various domains. Our innovative approach, Task-Centered Instruction Evolution (TaCIE), addresses these shortcomings by redefining instruction evolution from merely evolving seed instructions to a more dynamic and comprehensive combination of elements. TaCIE starts by deconstructing complex instructions into their fundamental components. It then generates and integrates new elements with the original ones, reassembling them into more sophisticated instructions that progressively increase in difficulty, diversity, and complexity. Applied across multiple domains, LLMs fine-tuned with these evolved instructions have substantially outperformed those tuned with conventional methods, marking a significant advancement in instruction-based model fine-tuning.
Bushfire Severity Modelling and Future Trend Prediction Across Australia: Integrating Remote Sensing and Machine Learning
Authors: Shouthiri Partheepan, Farzad Sanati, Jahan Hassan
Date: 2024-09-18 00:00:00
Tasks: Management

Bushfire is one of the major natural disasters that cause huge losses to livelihoods and the environment. Understanding and analyzing the severity of bushfires is crucial for effective management and mitigation strategies, helping to prevent the extensive damage and loss caused by these natural disasters. This study presents an in-depth analysis of bushfire severity in Australia over the last twelve years, combining remote sensing data and machine learning techniques to predict future fire trends. By utilizing Landsat imagery and integrating spectral indices like NDVI, NBR, and Burn Index, along with topographical and climatic factors, we developed a robust predictive model using XGBoost. The model achieved high accuracy, 86.13%, demonstrating its effectiveness in predicting fire severity across diverse Australian ecosystems. By analyzing historical trends and integrating factors such as population density and vegetation cover, we identify areas at high risk of future severe bushfires. Additionally, this research identifies key regions at risk, providing data-driven recommendations for targeted firefighting efforts. The findings contribute valuable insights into fire management strategies, enhancing resilience to future fire events in Australia. Also, we propose future work on developing a UAV-based swarm coordination model to enhance fire prediction in real-time and firefighting capabilities in the most vulnerable regions.
Enhancing Mental Health Support through Human-AI Collaboration: Toward Secure and Empathetic AI-enabled chatbots
Authors: Rawan AlMakinah, Andrea Norcini-Pala, Lindsey Disney, M. Abdullah Canbaz
Date: 2024-09-17 00:00:00
Tasks: Chatbot, Federated Learning

Access to mental health support remains limited, particularly in marginalized communities where structural and cultural barriers hinder timely care. This paper explores the potential of AI-enabled chatbots as a scalable solution, focusing on advanced large language models (LLMs)-GPT v4, Mistral Large, and LLama V3.1-and assessing their ability to deliver empathetic, meaningful responses in mental health contexts. While these models show promise in generating structured responses, they fall short in replicating the emotional depth and adaptability of human therapists. Additionally, trustworthiness, bias, and privacy challenges persist due to unreliable datasets and limited collaboration with mental health professionals. To address these limitations, we propose a federated learning framework that ensures data privacy, reduces bias, and integrates continuous validation from clinicians to enhance response quality. This approach aims to develop a secure, evidence-based AI chatbot capable of offering trustworthy, empathetic, and bias-reduced mental health support, advancing AI's role in digital mental health care.
OATH: Efficient and Flexible Zero-Knowledge Proofs of End-to-End ML Fairness
Authors: Olive Franzese, Ali Shahin Shamsabadi, Hamed Haddadi
Date: 2024-09-17 00:00:00
Tasks: Fairness

Though there is much interest in fair AI systems, the problem of fairness noncompliance -- which concerns whether fair models are used in practice -- has received lesser attention. Zero-Knowledge Proofs of Fairness (ZKPoF) address fairness noncompliance by allowing a service provider to verify to external parties that their model serves diverse demographics equitably, with guaranteed confidentiality over proprietary model parameters and data. They have great potential for building public trust and effective AI regulation, but no previous techniques for ZKPoF are fit for real-world deployment. We present OATH, the first ZKPoF framework that is (i) deployably efficient with client-facing communication comparable to in-the-clear ML as a Service query answering, and an offline audit phase that verifies an asymptotically constant quantity of answered queries, (ii) deployably flexible with modularity for any score-based classifier given a zero-knowledge proof of correct inference, (iii) deployably secure with an end-to-end security model that guarantees confidentiality and fairness across training, inference, and audits. We show that OATH obtains strong robustness against malicious adversaries at concretely efficient parameter settings. Notably, OATH provides a 1343x improvement to runtime over previous work for neural network ZKPoF, and scales up to much larger models -- even DNNs with tens of millions of parameters.
Fundamentals of legislation for autonomous artificial intelligence systems
Authors: Anna Romanova
Date: 2024-09-17 00:00:00
Tasks: Management

The article proposes a method for forming a dedicated operational context in course of development and implementation of autonomous corporate management systems based on example of autonomous systems for a board of directors. The significant part of the operational context for autonomous company management systems is the regulatory and legal environment within which corporations operate. In order to create a special operational context for autonomous artificial intelligence systems, the wording of local regulatory documents can be simultaneously presented in two versions: for use by people and for use by autonomous systems. In this case, the artificial intelligence system will get a well-defined operational context that allows such a system to perform functions within the required standards. Local regulations that provide for the specifics of the joint work of individuals and autonomous artificial intelligence systems can create the basis of the relevant legislation governing the development and implementation of autonomous systems.
Multidimensional Human Activity Recognition With Large Language Model: A Conceptual Framework
Authors: Syed Mhamudul Hasan
Date: 2024-09-16 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Language Modeling, Language Modelling, Large Language Model, Navigate

In high-stake environments like emergency response or elder care, the integration of large language model (LLM), revolutionize risk assessment, resource allocation, and emergency responses in Human Activity Recognition (HAR) systems by leveraging data from various wearable sensors. We propose a conceptual framework that utilizes various wearable devices, each considered as a single dimension, to support a multidimensional learning approach within HAR systems. By integrating and processing data from these diverse sources, LLMs can process and translate complex sensor inputs into actionable insights. This integration mitigates the inherent uncertainties and complexities associated with them, and thus enhancing the responsiveness and effectiveness of emergency services. This paper sets the stage for exploring the transformative potential of LLMs within HAR systems in empowering emergency workers to navigate the unpredictable and risky environments they encounter in their critical roles.
Strategic AI Governance: Insights from Leading Nations
Authors: Dian W. Tjondronegoro
Date: 2024-09-16 00:00:00

Artificial Intelligence (AI) has the potential to revolutionize various sectors, yet its adoption is often hindered by concerns about data privacy, security, and the understanding of AI capabilities. This paper synthesizes AI governance approaches, strategic themes, and enablers and challenges for AI adoption by reviewing national AI strategies from leading nations. The key contribution is the development of an EPIC (Education, Partnership, Infrastructure, Community) framework, which maps AI implementation requirements to fully realize social impacts and public good from successful and sustained AI deployment. Through a multi-perspective content analysis of the latest AI strategy documents, this paper provides a structured comparison of AI governance strategies across nations. The findings offer valuable insights for governments, academics, industries, and communities to enable responsible and trustworthy AI deployments. Future work should focus on incorporating specific requirements for developing countries and applying the strategies to specific AI applications, industries, and the public sector.