pwc-archive/papers-with-abstracts huggingface dataset:
Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare
Authors: Tianqi Shang, Weiqing He, Tianlong Chen, Ying Ding, Huanmei Wu, Kaixiong Zhou, Li Shen
Date: 2024-11-29 00:00:00
Tasks: Fairness, Knowledge Graphs, Link Prediction

Social determinants of health (SDoH) play a crucial role in patient health outcomes, yet their integration into biomedical knowledge graphs remains underexplored. This study addresses this gap by constructing an SDoH-enriched knowledge graph using the MIMIC-III dataset and PrimeKG. We introduce a novel fairness formulation for graph embeddings, focusing on invariance with respect to sensitive SDoH information. Via employing a heterogeneous-GCN model for drug-disease link prediction, we detect biases related to various SDoH factors. To mitigate these biases, we propose a post-processing method that strategically reweights edges connected to SDoHs, balancing their influence on graph representations. This approach represents one of the first comprehensive investigations into fairness issues within biomedical knowledge graphs incorporating SDoH. Our work not only highlights the importance of considering SDoH in medical informatics but also provides a concrete method for reducing SDoH-related biases in link prediction tasks, paving the way for more equitable healthcare recommendations. Our code is available at \url{https://github.com/hwq0726/SDoH-KG}.
Towards the Ultimate Programming Language: Trust and Benevolence in the Age of Artificial Intelligence
Authors: Bartosz Sawicki, Michał Śmiałek, Bartłomiej Skowron
Date: 2024-11-29 00:00:00

This article explores the evolving role of programming languages in the context of artificial intelligence. It highlights the need for programming languages to ensure human understanding while eliminating unnecessary implementation details and suggests that future programs should be designed to recognize and actively support user interests. The vision includes a three-level process: using natural language for requirements, translating it into a precise system definition language, and finally optimizing the code for performance. The concept of an "Ultimate Programming Language" is introduced, emphasizing its role in maintaining human control over machines. Trust, reliability, and benevolence are identified as key elements that will enhance cooperation between humans and AI systems.
Can an AI Agent Safely Run a Government? Existence of Probably Approximately Aligned Policies
Authors: Frédéric Berdoz, Roger Wattenhofer
Date: 2024-11-21 00:00:00
Tasks: AI Agent, Decision Making

While autonomous agents often surpass humans in their ability to handle vast and complex data, their potential misalignment (i.e., lack of transparency regarding their true objective) has thus far hindered their use in critical applications such as social decision processes. More importantly, existing alignment methods provide no formal guarantees on the safety of such models. Drawing from utility and social choice theory, we provide a novel quantitative definition of alignment in the context of social decision-making. Building on this definition, we introduce probably approximately aligned (i.e., near-optimal) policies, and we derive a sufficient condition for their existence. Lastly, recognizing the practical difficulty of satisfying this condition, we introduce the relaxed concept of safe (i.e., nondestructive) policies, and we propose a simple yet robust method to safeguard the black-box policy of any autonomous agent, ensuring all its actions are verifiably safe for the society.
Pre-Deployment Information Sharing: A Zoning Taxonomy for Precursory Capabilities
Authors: Matteo Pistillo, Charlotte Stix
Date: 2024-11-18 00:00:00

High-impact and potentially dangerous capabilities can and should be broken down into early warning shots long before reaching red lines. Each of these early warning shots should correspond to a precursory capability. Each precursory capability sits on a spectrum indicating its proximity to a final high-impact capability, corresponding to a red line. To meaningfully detect and track capability progress, we propose a taxonomy of dangerous capability zones (a zoning taxonomy) tied to a staggered information exchange framework that enables relevant bodies to take action accordingly. In the Frontier AI Safety Commitments, signatories commit to sharing more detailed information with trusted actors, including an appointed body, as appropriate (Commitment VII). Building on our zoning taxonomy, this paper makes four recommendations for specifying information sharing as detailed in Commitment VII. (1) Precursory capabilities should be shared as soon as they become known through internal evaluations before deployment. (2) AI Safety Institutes (AISIs) should be the trusted actors appointed to receive and coordinate information on precursory components. (3) AISIs should establish adequate information protection infrastructure and guarantee increased information security as precursory capabilities move through the zones and towards red lines, including, if necessary, by classifying the information on precursory capabilities or marking it as controlled. (4) High-impact capability progress in one geographical region may translate to risk in other regions and necessitates more comprehensive risk assessment internationally. As such, AISIs should exchange information on precursory capabilities with other AISIs, relying on the existing frameworks on international classified exchanges and applying lessons learned from other regulated high-risk sectors.
Effective Mitigations for Systemic Risks from General-Purpose AI
Authors: Risto Uuk, Annemieke Brouwer, Tim Schreier, Noemi Dreksler, Valeria Pulignano, Rishi Bommasani
Date: 2024-11-14 00:00:00

The systemic risks posed by general-purpose AI models are a growing concern, yet the effectiveness of mitigations remains underexplored. Previous research has proposed frameworks for risk mitigation, but has left gaps in our understanding of the perceived effectiveness of measures for mitigating systemic risks. Our study addresses this gap by evaluating how experts perceive different mitigations that aim to reduce the systemic risks of general-purpose AI models. We surveyed 76 experts whose expertise spans AI safety; critical infrastructure; democratic processes; chemical, biological, radiological, and nuclear risks (CBRN); and discrimination and bias. Among 27 mitigations identified through a literature review, we find that a broad range of risk mitigation measures are perceived as effective in reducing various systemic risks and technically feasible by domain experts. In particular, three mitigation measures stand out: safety incident reports and security information sharing, third-party pre-deployment model audits, and pre-deployment risk assessments. These measures show both the highest expert agreement ratings (>60\%) across all four risk areas and are most frequently selected in experts' preferred combinations of measures (>40\%). The surveyed experts highlighted that external scrutiny, proactive evaluation and transparency are key principles for effective mitigation of systemic risks. We provide policy recommendations for implementing the most promising measures, incorporating the qualitative contributions from experts. These insights should inform regulatory frameworks and industry practices for mitigating the systemic risks associated with general-purpose AI.
The Femininomenon of Inequality: A Data-Driven Analysis and Cluster Profiling in Indonesia
Authors: J. S. Muthmaina
Date: 2024-11-13 00:00:00

This study addresses the persistent challenges of Workplace Gender Equality (WGE) in Indonesia, examining regional disparities in gender empowerment and inequality through the Gender Empowerment Index (IDG) and Gender Inequality Index (IKG). Despite Indonesia's economic growth and incremental progress in gender equality, as indicated by improvements in the IDG and IKG scores from 2018 to 2023, substantial regional differences remain. Utilizing k-means clustering, the study identifies two distinct clusters of regions with contrasting gender profiles. Cluster 0 includes regions like DKI Jakarta and Central Java, characterized by higher gender empowerment and lower inequality, while Cluster 1 comprises areas such as Papua and North Maluku, where gender disparities are more pronounced. The analysis reveals that local socio-economic conditions and governance frameworks play a critical role in shaping regional gender dynamics. Correlation analyses further demonstrate that higher empowerment is generally associated with lower inequality and greater female representation in professional roles. These findings underscore the importance of targeted, region-specific interventions to promote WGE, addressing both structural and cultural barriers. The insights provided by this study aim to guide policymakers in developing tailored strategies to foster gender equality and enhance women's participation in the workforce across Indonesia's diverse regions.
A Clinical Trial Design Approach to Auditing Language Models in Healthcare Setting
Authors: Lovedeep Gondara, Jonathan Simkin
Date: 2024-11-11 00:00:00
Tasks: Language Modeling, Language Modelling

We present an audit mechanism for language models, with a focus on models deployed in the healthcare setting. Our proposed mechanism takes inspiration from clinical trial design where we posit the language model audit as a single blind equivalence trial, with the comparison of interest being the subject matter experts. We show that using our proposed method, we can follow principled sample size and power calculations, leading to the requirement of sampling minimum number of records while maintaining the audit integrity and statistical soundness. Finally, we provide a real-world example of the audit used in a production environment in a large-scale public health network.
Benefits and Risks of Using ChatGPT4 as a Teaching Assistant for Computer Science Students
Authors: Yaiza Aragonés-Soria, Julia Kotovich, Chitsutha Soomlek, Manuel Oriol
Date: 2024-11-08 00:00:00
Tasks: Chatbot

Upon release, ChatGPT3.5 shocked the software engineering community by its ability to generate answers to specialized questions about coding. Immediately, many educators wondered if it was possible to use the chatbot as a support tool that helps students answer their programming questions. This article evaluates this possibility at three levels: fundamental Computer Science knowledge (basic algorithms and data structures), core competency (design patterns), and advanced knowledge (quantum computing). In each case, we ask normalized questions several times to ChatGPT3.5, then look at the correctness of answers, and finally check if this creates issues. The main result is that the performances of ChatGPT3.5 degrades drastically as the specialization of the domain increases: for basic algorithms it returns answers that are almost always correct, for design patterns the generated code contains many code smells and is generally of low quality, but it is still sometimes able to fix it (if asked), and for quantum computing it is often blatantly wrong.
AI Ethics by Design: Implementing Customizable Guardrails for Responsible AI Development
Authors: Kristina Šekrst, Jeremy McHugh, Jonathan Rodriguez Cefalu
Date: 2024-11-05 00:00:00
Tasks: Ethics

This paper explores the development of an ethical guardrail framework for AI systems, emphasizing the importance of customizable guardrails that align with diverse user values and underlying ethics. We address the challenges of AI ethics by proposing a structure that integrates rules, policies, and AI assistants to ensure responsible AI behavior, while comparing the proposed framework to the existing state-of-the-art guardrails. By focusing on practical mechanisms for implementing ethical standards, we aim to enhance transparency, user autonomy, and continuous improvement in AI systems. Our approach accommodates ethical pluralism, offering a flexible and adaptable solution for the evolving landscape of AI governance. The paper concludes with strategies for resolving conflicts between ethical directives, underscoring the present and future need for robust, nuanced and context-aware AI systems.
Transforming Engineering Education Using Generative AI and Digital Twin Technologies
Authors: Yu-Zheng Lin, Ahmed Hussain J Alhamadah, Matthew William Redondo, Karan Himanshu Patel, Sujan Ghimire, Banafsheh Saber Latibari, Soheil Salehi, Pratik Satam
Date: 2024-11-02 00:00:00

Digital twin technology, traditionally used in industry, is increasingly recognized for its potential to enhance educational experiences. This study investigates the application of industrial digital twins (DTs) in education, focusing on how DT models of varying fidelity can support different stages of Bloom's taxonomy in the cognitive domain. We align Bloom's six cognitive stages with educational levels: undergraduate studies for "Remember" and "Understand," master's level for "Apply" and "Analyze," and doctoral level for "Evaluate" and "Create." Low-fidelity DTs aid essential knowledge acquisition and skill training, providing a low-risk environment for grasping fundamental concepts. Medium-fidelity DTs offer more detailed and dynamic simulations, enhancing application skills and problem-solving. High-fidelity DTs support advanced learners by replicating physical phenomena, allowing for innovative design and complex experiments. Within this framework, large language models (LLMs) serve as mentors, assessing progress, filling knowledge gaps, and assisting with DT interactions, parameter setting, and debugging. We evaluate the educational impact using the Kirkpatrick Model, examining how each DT model's fidelity influences learning outcomes. This framework helps educators make informed decisions on integrating DTs and LLMs to meet specific learning objectives.
A Comprehensive Study of Shapley Value in Data Analytics
Authors: Hong Lin, Shixin Wan, Zhongle Xie, Ke Chen, Meihui Zhang, Lidan Shou, Gang Chen
Date: 2024-12-02 00:00:00

Over the recent years, Shapley value (SV), a solution concept from cooperative game theory, has found numerous applications in data analytics (DA). This paper provides the first comprehensive study of SV used throughout the DA workflow, clarifying the key variables in defining DA-applicable SV and the essential functionalities that SV can provide for data scientists. We condense four primary challenges of using SV in DA, namely computation efficiency, approximation error, privacy preservation, and interpretability, then disentangle the resolution techniques from existing arts in this field, analyze and discuss the techniques w.r.t. each challenge and potential conflicts between challenges. We also implement SVBench, a modular and extensible open-sourced framework for developing SV applications in different DA tasks, and conduct extensive evaluations to validate our analyses and discussions. Based on the qualitative and quantitative results, we identify the limitations of current efforts for applying SV to DA and highlight the directions of future research and engineering.
A Self-Explainable Heterogeneous GNN for Relational Deep Learning
Authors: Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger
Date: 2024-11-30 00:00:00
Tasks: Deep Learning, Graph Neural Network

Recently, significant attention has been given to the idea of viewing relational databases as heterogeneous graphs, enabling the application of graph neural network (GNN) technology for predictive tasks. However, existing GNN methods struggle with the complexity of the heterogeneous graphs induced by databases with numerous tables and relations. Traditional approaches either consider all possible relational meta-paths, thus failing to scale with the number of relations, or rely on domain experts to identify relevant meta-paths. A recent solution does manage to learn informative meta-paths without expert supervision, but assumes that a node's class depends solely on the existence of a meta-path occurrence. In this work, we present a self-explainable heterogeneous GNN for relational data, that supports models in which class membership depends on aggregate information obtained from multiple occurrences of a meta-path. Experimental results show that in the context of relational databases, our approach effectively identifies informative meta-paths that faithfully capture the model's reasoning mechanisms. It significantly outperforms existing methods in both synthetic and real-world scenario.
An AI-Driven Data Mesh Architecture Enhancing Decision-Making in Infrastructure Construction and Public Procurement
Authors: Saurabh Mishra, Mahendra Shinde, Aniket Yadav, Bilal Ayyub, Anand Rao
Date: 2024-11-29 00:00:00
Tasks: Decision Making

Infrastructure construction, often dubbed an "industry of industries," is closely linked with government spending and public procurement, offering significant opportunities for improved efficiency and productivity through better transparency and information access. By leveraging these opportunities, we can achieve notable gains in productivity, cost savings, and broader economic benefits. Our approach introduces an integrated software ecosystem utilizing Data Mesh and Service Mesh architectures. This system includes the largest training dataset for infrastructure and procurement, encompassing over 100 billion tokens, scientific publications, activities, and risk data, all structured by a systematic AI framework. Supported by a Knowledge Graph linked to domain-specific multi-agent tasks and Q&A capabilities, our platform standardizes and ingests diverse data sources, transforming them into structured knowledge. Leveraging large language models (LLMs) and automation, our system revolutionizes data structuring and knowledge creation, aiding decision-making in early-stage project planning, detailed research, market trend analysis, and qualitative assessments. Its web-scalable architecture delivers domain-curated information, enabling AI agents to facilitate reasoning and manage uncertainties, while preparing for future expansions with specialized agents targeting particular challenges. This integration of AI with domain expertise not only boosts efficiency and decision-making in construction and infrastructure but also establishes a framework for enhancing government efficiency and accelerating the transition of traditional industries to digital workflows. This work is poised to significantly influence AI-driven initiatives in this sector and guide best practices in AI Operations.
Towards Data-centric Machine Learning on Directed Graphs: a Survey
Authors: Henan Sun, Xunkai Li, Daohan Su, Junyi Han, Rong-Hua Li, Guoren Wang
Date: 2024-11-28 00:00:00
Tasks: Graph Learning, Model Optimization, Survey

In recent years, Graph Neural Networks (GNNs) have made significant advances in processing structured data. However, most of them primarily adopted a model-centric approach, which simplifies graphs by converting them into undirected formats and emphasizes model designs. This approach is inherently limited in real-world applications due to the unavoidable information loss in simple undirected graphs and the model optimization challenges that arise when exceeding the upper bounds of this sub-optimal data representational capacity. As a result, there has been a shift toward data-centric methods that prioritize improving graph quality and representation. Specifically, various types of graphs can be derived from naturally structured data, including heterogeneous graphs, hypergraphs, and directed graphs. Among these, directed graphs offer distinct advantages in topological systems by modeling causal relationships, and directed GNNs have been extensively studied in recent years. However, a comprehensive survey of this emerging topic is still lacking. Therefore, we aim to provide a comprehensive review of directed graph learning, with a particular focus on a data-centric perspective. Specifically, we first introduce a novel taxonomy for existing studies. Subsequently, we re-examine these methods from the data-centric perspective, with an emphasis on understanding and improving data representation. It demonstrates that a deep understanding of directed graphs and their quality plays a crucial role in model performance. Additionally, we explore the diverse applications of directed GNNs across 10+ domains, highlighting their broad applicability. Finally, we identify key opportunities and challenges within the field, offering insights that can guide future research and development in directed graph learning.
Technical Report on Reinforcement Learning Control on the Lucas-Nülle Inverted Pendulum
Authors: Maximilian Schenke, Shalbus Bukarov
Date: 2024-12-03 00:00:00
Tasks: Decision Making, Reinforcement Learning (RL), Sequential Decision Making

The discipline of automatic control is making increased use of concepts that originate from the domain of machine learning. Herein, reinforcement learning (RL) takes an elevated role, as it is inherently designed for sequential decision making, and can be applied to optimal control problems without the need for a plant system model. To advance education of control engineers and operators in this field, this contribution targets an RL framework that can be applied to educational hardware provided by the Lucas-N\"ulle company. Specifically, the goal of inverted pendulum control is pursued by means of RL, including both, swing-up and stabilization within a single holistic design approach. Herein, the actual learning is enabled by separating corresponding computations from the real-time control computer and outsourcing them to a different hardware. This distributed architecture, however, necessitates communication of the involved components, which is realized via CAN bus. The experimental proof of concept is presented with an applied safeguarding algorithm that prevents the plant from being operated harmfully during the trial-and-error training phase.
Towards the efficacy of federated prediction for epidemics on networks
Authors: Chengpeng Fu, Tong Li, Hao Chen, Wen Du, Zhidong He
Date: 2024-12-03 00:00:00
Tasks: Federated Learning, Graph Attention, graph partitioning, Prediction, Privacy Preserving

Epidemic prediction is of practical significance in public health, enabling early intervention, resource allocation, and strategic planning. However, privacy concerns often hinder the sharing of health data among institutions, limiting the development of accurate prediction models. In this paper, we develop a general privacy-preserving framework for node-level epidemic prediction on networks based on federated learning (FL). We frame the spatio-temporal spread of epidemics across multiple data-isolated subnetworks, where each node state represents the aggregate epidemic severity within a community. Then, both the pure temporal LSTM model and the spatio-temporal model i.e., Spatio-Temporal Graph Attention Network (STGAT) are proposed to address the federated epidemic prediction. Extensive experiments are conducted on various epidemic processes using a practical airline network, offering a comprehensive assessment of FL efficacy under diverse scenarios. By introducing the efficacy energy metric to measure system robustness under various client configurations, we systematically explore key factors influencing FL performance, including client numbers, aggregation strategies, graph partitioning, missing infectious reports. Numerical results manifest that STGAT excels in capturing spatio-temporal dependencies in dynamic processes whereas LSTM performs well in simpler pattern. Moreover, our findings highlight the importance of balancing feature consistency and volume uniformity among clients, as well as the prediction dilemma between information richness and intrinsic stochasticity of dynamic processes. This study offers practical insights into the efficacy of FL scenario in epidemic management, demonstrates the potential of FL to address broader collective dynamics.